Data, Research & Analytics

Career path Bringing careers journey to life

Contents

Functional/Technical Competencies

Lateral and Ladder career moves The career moves lattice Stretch assignments

Persona Utilisation

The RELX Competencies

Acknowledgement

Career Path Career Framework Career Structure

Career Path @ RELX

3

Appendix

Data Research & Analytics

Job Family: Decision Scientist

Job Family: Data Scientist

RELX Job Family

Job Family: Data Analyst

Workday Overview

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Acknowledgement

5

By Sponsor & the working group/forum members

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Career path At RELX , we want our employee to be fully engaged in their professional growth and recognise the interplay between their success and our Organisation’s success. We want to enable our people to actively drive their personal growth and realise their career aspirations at RELX, as we continue to evolve critical capabilities and roles across the Organisation.

Your career path is made up of the positions you hold as you grow in your career. Your first job or college degree, for example, can mark the beginning of your career path. As you gain additional knowledge and skills, you may choose to move vertically into more senior roles or deepen your expertise by moving laterally into equal but different job roles as you specialise. Or you may choose an entirely different direction for your future.

Career Path

7

As we develop our CAREER PATHS we will provide tools to help you to clarify your aspirations and development. So you can build and strengthen the critical capabilities needed to drive growth for you and our Organisation. The paths answer three commonly asked questions: To aid you in your career choices, we have developed our RELX CAREER PATHS which: • Demonstrate ‘typical’ career journeys/experiences/expertise and competencies of successful employees at RELX. • Define role profiles. Illustrating how responsibilities evolve within a given job family, and how capability requirements evolve to match those responsibilities.

What’s critical to my success?

What are the expected core technical and behaviour competencies in this role?

2

3

1

How do I measure my success?

Persona Hugh Jackman

Joined the company 6 years ago

Internet Marketing Specialist

Search Engine Marketing Manager

Marketing Coordinator

Passionate about new Technologies

2 years

3 years

Now

Wants to stretch his career, building on his strengths, skills and experiences. Where could he go , and what could he do in the next 2 years?

Aspires to be either CMO or Social Media Director

Recently, Hugh moved laterally into the Digital Marketing Manager role within his target timeframe.

Creative, problem- solver

9

Given Hugh’s aspirations, which: • Path/route/role should he pursue? • What capabilities should he develop?

Demand Generation Manager

Demand Generation Director

Search Engine Marketing Manager

Marketing Coordinator

Internet Marketing Specialist

Vice President of Marketing

Digital Marketing Manager

Marketing Director

Chief Marketing Officer

Social Media Coordinator

Watch it

Social Media Director

Social Media Manager

Framework Utilisation How do employees use career frameworks?

Where do I want to go? What is my career goal? Is it within this framework or in a related area?

3

1

2

Where qualities do I possess? Which skills and competencies do I have that are included on the framework?

Where am I? Exactly where do I fit into the area I work and what do I provide?

11

What do I need to do? How do I develop new skills and competencies to progress? What is my plan?

4

5

Support to get there? I need to discuss my plan with my manager and create a career development plan

Career path

Functional/technical competencies

Job family

+

Job code

Job description

Career path

+

=

Job level

+

Behavioural competencies

13

Career framework

Concept

Enablers

Connect

• Clarify the purpose of the job and key responsibilities • Prepare for annual performance planning • As a basis for recruitment and selection interviews

Performance Plan

Core Responsibilities

( All about me )

• Outline required technical and behaviour skills profile • Identify training and development needs • Prepare for development planning discussions • Assess capability for job moves

Development Plan

Core Capabilities

• Identify a range of developmental career experiences • Prepare for career planning discussions • Evaluate future career choices aligned to aspirations

Career Plan

Career path

Career Structure

Career path

Core Responsibility

Required capabilities

Career level and type

Job interest

+

+

Role definition and key responsibilities

Competencies assessment

Technical skills

+

+

+

External hire: preferred academic background & professional experience

Development planning

RELX Competencies

Technical/functional competencies

15

Example: Project Management

Applies basic principles, tools and techniques of project management in the area of operation.

Level 1

Skills and knowledge that are critical to the role. Proficiencies, detailed across four levels, capture the degree of complexity across roles.

What are functional/ technical skills?

Executes projects according to plan, tracking progress using the available project management tools and techniques.

Level 2

Develops project plans and leads projects including monitoring adherence to timelines, resources and budgets for maximizing productivity.

Level 3

Competency assessments provide a 'gap analysis’ between individual’s capability and the requirements of the role being considered. This feeds development planning. Performance measures gauge the overall capability of an individual in the short/immediate-term. Developing relevant competencies builds longer-term, transferable skills that improve contribution and career potential.

How are skills linked to career growth?

Identifies project workstreams from strategic business needs. Leads multiple projects or guides planning, execution, and completion of large-scale projects.

Level 4

Leads and accountable for multiple projects workstream, execution, and completion of large-scale projects.

Why develop skills?

Level 5

Project management competency

RELX Competencies

17

Focus on Results

Leading Change Develops Strategic Perspectives Champions Change Connects the Group to the Outside World

Personal Capability Technical/ Professional Expertise Solves Problems and Analyzes Issues Innovates

Interpersonal Skills

Character

Communicates Powerfully and Prolifically Inspires and Motivates Others to High Performance Builds Relationships

Displays High Integrity and Honesty

Drives for Results Establishes Stretch Goals Takes Initiative Makes Decisions Takes Risks

Develops Others Collaboration and Teamwork

Practices Self- Development

Read it

Lateral and Ladder career moves

19

The career moves lattice

Top tip

Vertical

Increasing responsibility in Organisation

Change job within organisation, to gain new skills or experiences No change in pay/status//level of responsibility

Change job within organisation, to gain new skills or experiences No change in pay/status/level of responsibility

Lateral

Lateral

Growing within existing job

Enrichment

Step back from existing responsibility/pay/status to pursue different career options

Realignment

Stretch assignments

Read it

Assignments can involve:

Learning in diverse environments accelerates development and, through exposure to senior management, can increase the scope of opportunities. Stretch assignments provide opportunities for individuals to accelerate the development of their business, technical and leadership capabilities through exposure to new experiences in a different business/cultural context.

Part-time projects in addition to current responsibilities

Full-time projects / job assignments on a short or medium- term basis

21

Comfort zone Getting the balance right Panic zone

Development zone

Skills are almost a perfect match for the situation Effective performance for a time but little opportunity for growth

Demands of situation are significantly different from the individual’s current skills/abilities Substantial demands create high stress and can lead to ‘paralysis’

Individual has a reasonable level of necessary skills/abilities to handle the demands of the situation Sufficient stretch to support development of the new skills required to meet the demands of the role/challenge Support is available to aid the development of the new requirements

Demands of the role

Current skills /abilities

Data, Research & Analytics

Data, Research & Analytics

23

Data Science

Data Analytics

Decision Science

Data Analysts consult to understand problems, collect, and analyse data to support business decisions. They do this through the use of data tools to collate, model, interpret, develop visualisations/ information products and communicate to the business.

Data Scientists create product features and capabilities for our business through modelling data. They utilize a bottom-up approach to analytics and modelling, starting with the data (sometimes big data) and frequently utilize modelling methods such as data reduction, classification, and machine learning. Data scientist’s duties typically include creating various machine learning-based tools or processes within the company, such as recommendation engines, machine automation process, tool/software creation or machine intelligent products (such as chatbots).

Decision scientists create analytic and insight models to drive business decisions for internal or external customers. They typically use a top-down approach of applying statistical models to well constructed datasets providing decision tools and insights for the business. These statistical methods include the full range of techniques, but most often focus on predictive methods such as descriptive statistics, regression, and machine learning.

Job Family: Data Science

Data Science

25

Functional/Technical Competencies Job family: Data Science

Data Skills

Modelling Skills

Coding Skills

Project Management

Domain/Industry skills

Behaviour competencies

Drives for results Solves problems and analyzes issues

Collaboration and teamwork Practices self-development Takes initiative Innovates Technical/Professional expertise Establishes stretch goals Inspires and motivates others to high performance Builds relationships Develops others Communicates powerfully and prolifically Develops strategic perspectives Champions change Connects the group to the outside world

Data Science

Individual

Manager

Executive

Senior Manager

27

Data Science framework

Individual contributor

12 Senior Data Scientist II

13 Principal Data Scientist

14 Senior Principal Data Scientist

15 Chief Methodologist

8 Data Scientist I

9 Data Scientist II

10 Data Scientist III

11 Senior Data Scientist I

People manager

12 Lead Data Scientist

13 Manager Data Scientist

14 Senior Manager Data Scientist

15 Director/Head of Data Scientist

16 Senior Director/Head of Data Scientist

17 VP Data Scientist

Data Science role definitions

Data Scientist I

Data Scientist II

Data Scientist III

Senior Data Scientist I

Job code

8

9

10

11

Level

Data Scientist III should understand best practices and processes and be able to execute them independently, reviewing requirements and results with supervisors. Individuals in this role should work with managers to define project scope, and then execute the necessary methods to develop, test and deliver outcomes.

This is the entry- level role for a Data Scientist. Individuals in this role will provide support to the Data Scientist team while developing and advancing their analytic skills and team capabilities. Support will consist of data collection, manipulation, and model development to learn team best practices.

Data Scientist II should have a basic understanding of best practices, and can execute the methods for projects with oversight. Individuals in this role will continue to provide support to the Data Scientist team members and also begin to lead some analytic efforts.

Senior Data Scientist I should be able to define scope of a project with support of managers, and execute that project independently. Individuals in this role can also support the development and training of junior staff. Senior Data Scientist I should be self sufficient in executing basic methods, and work within their teams to execute increasingly sophisticated approaches to deliver outcomes. They should also support the development of best practices.

Individual Contributors

Data Science

29

Data Science role definitions

Principle Data Scientist

Senior Principle Data Scientist

Chief Methodologist

Senior Data Scientist II

Job code

12

13

14

15

Level

Senior Principal Data Scientists define projects and their execution. While they may have small teams of in-direct reports, they lead through influence and their focus is on mentoring teams to develop and execute on best practices. They are the methodological experts and are expected to raise the data-driven decision making bar of the entire Organisation.

Principal Data Scientists are emerging subject matter experts in their domain, who define projects and their execution. While they may have small teams of in- direct reports, they lead through influence and their focus is on mentoring teams to develop and execute on best practices and methodologies.

Senior Data Scientist II should be able to define scope of a project and execute that project independently. Individuals in this role are expected to support the development and training of junior staff. They develop best practices and are the project leaders.

Chief Methodologist is the recognized subject matter guru not only internally but also externally. They are the methodological experts and are expected to raise the data-driven decision making bar of the entire Organisation. They are also client facing and industry-facing and are expected to represent our highest level of analytical thinking.

Individual Contributors

Data Science role definitions

Manager Data Science

Senior Manager Data Science

Director Data Science

VP Data Science

Lead Data Scientist

Senior Director Data Scientist

Job code

12

13

14

15

16

17

Level

Senior Manager Data Scientists work with their team to define and execute projects to develop and execute on best practices. They are methodological experts to enhance products and services developed and supported by the team. Teams should function as change agents to insert best practices into workflows.

Manager Data Scientist are emerging subject matter experts in their domain. They lead a team of junior members to support their development and work product. They are mindful of best practices, and train their team in the execution of those best practices. Manages a team to define new best practices and innovative approaches to new business problems or use cases.

Leads a team of junior members to support their development and work product. Defines scope of a project and executes that project as a team. Supports the development and training of junior members. They develop best practices as team leaders.

Director of Decision Scientists are the methodological experts and through team leadership raise the data-driven decision making of a function. They are also client and industry-facing and represent the collective applied and/or theoretical analytical knowledge of the team. They lead people and teams to develop an overall strategy for the execution of projects and best practices. Influence should extend outside the director's immediate team to other teams within the director's peers.

Senior Director of Data Science are the methodological experts and through team leadership raise the data- driven decision making of the entire Business Unit. They are also client and industry-facing and represent the collective applied and/or theoretical knowledge as a thought leader to the industry. Lead people and teams to develop an overall strategy for the execution of projects and best practices. Influence should extend beyond immediate teams, to influence stakeholders and teams across the business unit.

VP Data Scientist are the methodological experts and through team leadership raise the data-driven decision making of the Enterprise wide. They are also client and industry-facing and represent the collective applied and/or theoretical knowledge as a change agent to the industry. Manage and execute a broad strategy that applies decision sciences to the unit's business strategy.

People manager

Data Science

31

Data Scientist role definitions Expectations 1 2 3

5

4

• Primarily provide support to more senior Data Scientists • Pull data, clean data, build and maintain existing models • Participate in result presentation to internal stakeholders

• Primary driver of analytic project and result • Lead code reviews, final approvers of models before reaching customer, set high- bar for model development • Evaluate and create new frameworks and define methodologies/ governance • Manage small-medium sized teams (direct or indirect) At least an undergraduate degree in relevant field and 4+ years of relevant work experience. Or a Masters Degree in a relevant field and 2+ years of relevant work experience. Or a PhD in a relevant field.

• Execute team's Data Science best practices • Mentor support to more junior Data Scientist • Primary developers of models/analytics • Build models, perform analytics, create AI features • Present results to internal stakeholders • Some interaction with customers/clients At least an undergraduate degree in relevant field and 2+ years of relevant work experience. Or a Masters Degree in a relevant field.

• Sets strategy and team direction; has clear vision for Data Science • Recognized expert in Data Science and several years experience managing larger Technical teams (direct or indirect) At least a Bachelor degree and 10+ years of relevant work experience. Or a Masters Degree in a relevant field and 5+ years of relevant work experience. Or a PhD in a relevant field with 2+ years of relevant work experience.

• As per Level 4 and • Manages P&L if needed

Role and responsibility

At least an undergraduate degree in relevant field. Some relevant work experience is a plus but not required

External Hire : Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field and 15+ years of relevant work experience. Or a Masters Degree in a relevant field and 10+ years of relevant work experience. Or a PhD in a relevant field with 5+ years of relevant work experience.

Modelling skills

Level 1 Basic skills

Level 3 Basic independence

Level 5 Develop new best practices

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Able to independently apply Machine Learning methods to build advanced models (e.g. deep learning) and roll into production. Able to build or test new modelling methods with senior guidance.

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Build best-practices around the usage of Data Science frameworks. Mentor others in building models and using Data Science frameworks.

Build competencies in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow). Able to apply Machine Learning methods to build standard models and update existing models.

Data Science

Level 2 Basic best practices

Level 4 More advance independence, lead others

Demonstrate competencies in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow). Able to apply Machine Learning methods to build advanced models (e.g. deep learning). Able to roll into production with senior guidance.

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Able to build best-practices around the usage of Data Science frameworks.

Return to Technical/ Functional Skill

33

Data skills

Level 1 Basic skills

Level 3 Basic independence

Level 5 Develop new best practices

Understand and utilize typical data steps to prepare and process data for analysis.

Own best practices in the development and use of data processes.

Independently create typical data steps to prepare and process data for analysis. Understand and utilize novel data steps to prepare and process data for analysis.

Level 2 Basic best practices

Level 4 More advance independence, lead others

Understand and create typical data steps to prepare and process data for analysis.

Create and utilize novel data steps to prepare and process data for analysis. Oversees others in the application of data steps.

Coding skills

Level 1 Basic skills

Level 3 Basic independence

Level 5 Develop new best practices

Own best practices in the coding languages used in Data Science (e.g. Python, SQL, Java, C++, and/or Scala). Explore new best practices in non-standard coding languages used in Data Science (e.g. Julia, Haskell).

Proficient in coding using scripting languages used in Data Science like Python, SQL, and/or Java.

Work across coding languages used in Data Science (e.g. Python, SQL, Java, C++, and/or Scala). Choosing the right coding language to implement the solution to answer the business questions.

Data Science

Level 2 Basic best practices

Level 4 More advance independence, lead others

Proficient in coding using one or more scripting languages used in Data Science like Python, SQL, and/or Java.

Work across coding languages used in Data Science (e.g. Python, SQL, C++, Java, and/or Scala). Expert in one or more of the languages. Overseeing others in use of the code, provide code reviews.

Return to Technical/ Functional Skill

35

Project management

Level 1 Basic skills

Level 3 Basic independence

Level 5 Develop new best practices

Basic project management of small scale analytics projects with overall planning and support from senior leaders

Develop project milestones and steps and independently execute for the entire small scale projects. Independently execute project steps for more complicated project management tasks.

Capable of developing a project plan and managing one or more junior employees in the execution of that project plan.

Level 2 Basic best practices

Level 4 More advance independence, lead others Develop small and medium complexity project plans. Oversee others in the execution of project tasks.

Independent support of small scale projects. Support from others in supporting more complicated project steps.

Domain/industry skills

Level 1 Basic skills

Level 3 Basic independence

Level 5 Develop new best practices

Use domain expertise to influence solutions that address market trends future dynamics of the discipline, industry and domains. Can generalise dynamics and transfer to other disciplines, industries and domains.

Understand and execute basic methods of the discipline, industry and domain.

Expertise in the basic methods common to the discipline, industry and domain. Develops a basic understanding of adjacent approaches common to the disciplines, industries and domain.

Data Science

Level 2 Basic best practices

Level 4 More advance independence, lead others

Understand and execute basic methods of the discipline, industry and domain.

Able to apply domain expertise to capture current and likely market trends in discipline, industry and domain. Has a holistic overview of adjacent disciplines, industries and domain.

Return to Technical/ Functional Skill

37

Data Science proficiencies

External Hire : Preferred Academic Background and Professional Experience

Project Management Industry Skill

Role and Responsibility

Individual Manager

Job Code

Modelling Skills Data skill

Coding Skills

Level 1 Level 1 Level 2 Level 2 Level 3 Level 3 Level 4 Level 5 Level 3 Level 3 Level 4 Level 5 L l l l l l l l l l l L l l

Level 1 Level 1 Level 1 Level 2 Level 2 Level 3 Level 3 Level 4 Level 3 Level Lev l Lev l l Lev l L l Lev l l l Level 3 Level 4 Level 4 l l 4 l

Level 1 Level 1 Level 1 Level 3 Level 3 Level 3 Level 5 Level 5 Level 3 Level 3 Level 5 Level 5 L l l l l l l l l l l L l l

Level 1 Level 2 Level 3 Level 3 Level 3 Level 4 Level 5 Level 5 Level 3 Level 4 Level 5 Level 5 L l L l L l L l L l L l L l L l L l l L v l l

l

l

l

8 9

Data Scientist I Data Scientist II Data Scientist III

Level 1 Level 2 Level 2 Level 3 Level 4 Level 5 Level 5 Level 5 Level 3 Level 3 Level 4 Level 5 l l l l l l l l l l l

Level 1 Level 2 Level 3 Level 3 Level 4 Level 5 Level 5 Level 5 Level 3 Level 4 Level 5 Level 5 l l l l l l l l l l l

Level 1 Level 2 Level 3 Level 3 Level 4 Level 5 Level 5 Level 5 Level 3 Level 4 Level 5 Level 5 l l l l l l l l l L l l

10 11 12 13 14 15

Senior Data Scientist I Senior Data Scientist II Principle Data Scientist Chief Methodologist Lead Data Scientist Manager Data Science Senior Principle Data Scientist Senior Manager Data Science

12 13 14 15

Director Data Science

Data Scientist I

8

Level

Individual

Level type

This is the entry-level role for a Data Scientist. Individuals in this role will provide support to the Data Scientist team while developing and advancing their analytic skills and team capabilities. Support will consist of data collection, manipulation, and model development to learn team best practices. Primarily provide support to more senior Data Scientists Pull data, clean data, build and maintain existing models Participate in result presentation to internal stakeholders

Role definition

Data Science

Scope & key responsibility

External hire: Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field. Some relevant work experience is a plus but not required.

Return to Data Science Framework

39

Data Scientist I

Level

Description

1

Modelling Skills

Build competencies in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow). Able to apply Machine Learning methods to build standard models and update existing models.

1

Data Skills

Understand and utilize typical data steps to prepare and process data for analysis

1

Coding Skills

Proficient in coding using scripting languages used in Data Science like Python, SQL, and/or Java.

1

Basic project management of small scale analytics projects with overall planning and support from senior leaders.

Project Management

Technical Skills

1

Industry/Domain Skills

Understand and execute basic methods of the discipline, industry and domain.

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Data Scientist II

9

Level

Individual

Level type

Data Scientist II should have a basic understanding of best practices, and can execute the methods for projects with oversight. Individuals in this role will continue to provide support to the Data Scientist team members and also begin to lead some analytic efforts.

Role definition

Data Science

Primarily provide support to more senior Data Scientists Pull data, clean data, build and maintain existing models

Participate in result presentation to internal stakeholders

Scope & key responsibility

External hire: Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field and 2+ years of relevant work experience. Or a Masters Degree in a relevant field.

Return to Data Science Framework

41

Data Scientist II

Level

Description

2

Modelling Skills

Demonstrate competencies in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow). Able to apply Machine Learning methods to build advanced models (e.g. deep learning). Able to roll into production with senior guidance.

2

Data Skills

Understand and create typical data steps to prepare and process data for analysis.

2

Coding Skills

Proficient in coding using one or more scripting languages used in Data Science like Python, SQL, and/or Java.

1

Basic project management of small scale analytics projects with overall planning and support from senior leaders.

Project Management

Technical Skills

1

Industry/Domain Skills

Understand and execute basic methods of the discipline, industry and domain.

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Data Scientist III

10

Level

Individual

Level type

Data Scientist III should understand best practices and processes and be able to execute them independently, reviewing requirements and results with supervisors. Individuals in this role should work with managers to define project scope, and then execute the necessary methods to develop, test and deliver outcomes. Primarily provide support to more senior Data Scientists Pull data, clean data, build and maintain existing models Participate in result presentation to internal stakeholders

Role definition

Data Science

Scope & key responsibility

External hire: Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field and 4+ years of relevant work experience. Or a Masters Degree in a relevant field and 2+ years of relevant work experience. Or a PhD in a relevant field.

Return to Data Science Framework

43

Data Scientist III

Level

Description

2

Modelling Skills

Demonstrate competencies in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow). Able to apply Machine Learning methods to build advanced models (e.g. deep learning). Able to roll into production with senior guidance. Independently create typical data steps to prepare and process data for analysis. Understand and utilize novel data steps to prepare and process data for analysis

3

Data Skills

3

Work across coding languages used in Data Science (e.g. Python, SQL, Java, C++, and/or Scala). Choosing the right coding language to implement the solution to answer the business questions

Coding Skills

2

Independent support of small scale projects. Support from others in supporting more complicated project steps.

Project Management

Technical Skills

1

Industry/Domain Skills

Understand and execute basic methods of the discipline, industry and domain.

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Senior Data Scientist I

11

Level

Individual

Level type

Senior Data Scientist I should be able to define scope of a project with support of managers, and execute that project independently. Individuals in this role can also support the development and training of junior staff. Senior Data Scientist I should be self sufficient in executing basic methods, and work within their teams to execute increasingly sophisticated approaches to deliver outcomes. They should also support the development of best practices. Execute team's Data Science best practices Mentor support to more junior Data Scientist Build models, perform analytics, create AI features Present results to internal stakeholders

Role definition

Data Science

Scope & key responsibility

Primary developers of models/analytics

Some interaction with customers/clients

External hire: Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field and 4+ years of relevant work experience. Or a Masters Degree in a relevant field and 2+ years of relevant work experience. Or a PhD in a relevant field.

Return to Data Science Framework

45

Senior Data Scientist I

Level

Description

3

Modelling Skills

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Able to independently apply Machine Learning methods to build advanced models (e.g. deep learning) and roll into production. Able to build or test new modelling methods with senior guidance Independently create typical data steps to prepare and process data for analysis. Understand and utilize novel data steps to prepare and process data for analysis

3

Data Skills

3

Work across coding languages used in Data Science (e.g. Python, SQL, Java, C++, and/or Scala). Choosing the right coding language to implement the solution to answer the business questions

Coding Skills

2

Independent support of small scale projects. Support from others in supporting more complicated project steps.

Project Management

Technical Skills

Expertise in the basic methods common to the discipline, industry and domain. Develops a basic understanding of adjacent approaches common to the disciplines, industries and domain.

3

Industry/Domain Skills

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Senior Data Scientist II

12

Level

Individual

Level type

Senior Data Scientist II should be able to define scope of a project and execute that project independently. Individuals in this role are expected to support the development and training of junior staff. They develop best practices and are the project leaders.

Role definition

Data Science

Execute team's Data Science best practices Mentor support to more junior Data Scientist

Build models, perform analytics, create AI features

Scope & key responsibility

Present results to internal stakeholders

Primary developers of models/analytics

Some interaction with customers/clients

External hire: Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field and 4+ years of relevant work experience. Or a Masters Degree in a relevant field and 2+ years of relevant work experience. Or a PhD in a relevant field.

Return to Data Science Framework

47

Senior Data Scientist II

Level

Description

4

Modelling Skills

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Able to build best-practices around the usage of Data Science frameworks.

4

Create and utilize novel data steps to prepare and process data for analysis. Oversees others in the application of data steps.

Data Skills

4

Work across coding languages used in Data Science (e.g. Python, SQL, C++, Java, and/or Scala). Expert in one or more of the languages. Overseeing others in use of the code, provide code reviews.

Coding Skills

3

Develop project milestones and steps and independently execute for the entire small scale projects. Independently execute project steps for more complicated project management tasks.

Project Management

Technical Skills

3

Industry/Domain Skills

Expertise in the basic methods common to the discipline, industry and domain. Develops a basic understanding of adjacent approaches common to the disciplines, industries and domain.

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Principal Data Scientist

13

Level

Individual

Level type

Principal Data Scientists are emerging subject matter experts in their domain, who define projects and their execution. While they may have small teams of in-direct reports, they lead through influence and their focus is on mentoring teams to develop and execute on best practices and methodologies.

Role definition

Data Science

Primary driver of analytic project and result Lead code reviews, final approvers of models before reaching customer, set high-bar for model development

valuate and create new frameworks and define methodologies/ governance

Scope & key responsibility

Manage small-medium sized teams (direct or indirect)

External hire: Preferred Academic Background and Professional Experience

At least a Bachelor degree and 10+ years of relevant work experience. Or a Masters Degree in a relevant field and 5+ years of relevant work experience. Or a PhD in a relevant field with 2+ years of relevant work experience.

Return to Data Science Framework

49

Principal Data Scientist

Level

Description

5

Modelling Skills

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Build best- practices around the usage of Data Science frameworks. Mentor others in building models and using Data Science frameworks.

5

Data Skills

Own best practices in the development and use of data processes.

5

Own best practices in the coding languages used in Data Science (e.g. Python, SQL, Java, C++, and/or Scala). Explore new best practices in non-standard coding languages used in Data Science (e.g. Julia, Haskell). Develop project milestones and steps and independently execute for the entire small scale projects. Independently execute project steps for more complicated project management tasks.

Coding Skills

3

Project Management

Technical Skills

3

Industry/Domain Skills

Understand and execute basic methods of the discipline, industry and domain.

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Senior Principal Data Scientist

14

Level

Individual

Level type

Senior Principal Data Scientists define projects and their execution. While they may have small teams of in-direct reports, they lead through influence and their focus is on mentoring teams to develop and execute on best practices. They are the methodological experts and are expected to raise the data-driven decision making bar of the entire organization.

Role definition

Data Science

Primary driver of analytic project and result Lead code reviews, final approvers of models before reaching customer, set high-bar for model development

Evaluate and create new frameworks and define methodologies/ governance

Scope & key responsibility

Manage small-medium sized teams (direct or indirect)

External hire: Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field and 15+ years of relevant work experience. Or a Masters Degree in a relevant field and 10+ years of relevant work experience. Or a PhD in a relevant field with 5+ years of relevant work experience.

Return to Data Science Framework

51

Senior Principal Data Scientist

Level

Description

5

Modelling Skills

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Build best- practices around the usage of Data Science frameworks. Mentor others in building models and using Data Science frameworks.

5

Data Skills

Own best practices in the development and use of data processes.

5

Own best practices in the coding languages used in Data Science (e.g. Python, SQL, Java, C++, and/or Scala). Explore new best practices in non-standard coding languages used in Data Science (e.g. Julia, Haskell).

Coding Skills

4

Project Management

Develop small and medium complexity project plans. Oversee others in the execution of project tasks.

Technical Skills

5

Industry/Domain Skills

Use domain expertise to influence solutions that address market trends future dynamics of the discipline, industry and domains. Can generalise dynamics and transfer to other disciplines, industries and domains.

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Chief Methodologist

15

Level

Individual

Level type

Chief Methodologist is the recognized subject matter guru not only internally but also externally. They are the methodological experts and are expected to raise the data-driven decision making bar of the entire organization. They are also client facing and industry-facing and are expected to represent our highest level of analytical thinking.

Role definition

Data Science

Recognized expert in Data Science and several years experience managing larger Technical teams (direct or indirect)

Sets strategy and team direction; has clear vision for Data Science

Scope & key responsibility

External hire: Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field and 15+ years of relevant work experience. Or a Masters Degree in a relevant field and 10+ years of relevant work experience. Or a PhD in a relevant field with 5+ years of relevant work experience.

Return to Data Science Framework

53

Chief Methodologist

Level

Description

5

Modelling Skills

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Build best- practices around the usage of Data Science frameworks. Mentor others in building models and using Data Science frameworks.

5

Data Skills

Own best practices in the development and use of data processes.

5

Own best practices in the coding languages used in Data Science (e.g. Python, SQL, Java, C++, and/or Scala). Explore new best practices in non-standard coding languages used in Data Science (e.g. Julia, Haskell). Capable of developing a project plan and managing one or more junior employees in the execution of that project plan.

Coding Skills

5

Project Management

Technical Skills

Use domain expertise to influence solutions that address market trends future dynamics of the discipline, industry and domains. Can generalise dynamics and transfer to other disciplines, industries and domains.

5

Industry/Domain Skills

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Lead Data Scientist

12

Level

Manger

Level type

Leads a team of junior members to support their development and work product. Defines scope of a project and executes that project as a team. Supports the development and training of junior members. They develop best practices as team leaders.

Role definition

Data Science

Primary driver of analytic project and result Lead code reviews, final approvers of models before reaching customer, set high-bar for model development

Evaluate and create new frameworks and define methodologies/ governance

Scope & key responsibility

Manage small-medium sized teams (direct or indirect)

External hire: Preferred Academic Background and Professional Experience

At least an undergraduate degree in relevant field and 4+ years of relevant work experience. Or a Masters Degree in a relevant field and 2+ years of relevant work experience. Or a PhD in a relevant field

Return to Data Science Framework

55

Lead Data Scientist

Level

Description

3

Modelling Skills

Expert in relevant Data Science frameworks (e.g. ScikitLearn, Tensorflow, Keras, Caffe, etc.). Able to independently apply Machine Learning methods to build advanced models (e.g. deep learning) and roll into production. Able to build or test new modelling methods with senior guidance. Independently create typical data steps to prepare and process data for analysis. Understand and utilize novel data steps to prepare and process data for analysis. Work across coding languages used in Data Science (e.g. Python, SQL, Java, C++, and/or Scala). Choosing the right coding language to implement the solution to answer the business questions.

3

Data Skills

3

Coding Skills

3

Develop project milestones and steps and independently execute for the entire small scale projects. Independently execute project steps for more complicated project management tasks.

Project Management

Technical Skills

Expertise in the basic methods common to the discipline, industry and domain. Develops a basic understanding of adjacent approaches common to the disciplines, industries and domain.

3

Industry/Domain Skills

x x x x x x

Collaboration and Teamwork Drives for Results Innovates Practices Self-Development Solves Problems and Analyses Issue Takes Initiative

ZF Behaviour Skills

x

Technical/Professional Expertise

Manager Data Scientist

13

Level

Manager

Level type

Manager Data Scientist are emerging subject matter experts in their domain. They lead a team of junior members to support their development and work product. They are mindful of best practices, and train their team in the execution of those best practices. Manages a team to define new best practices and innovative approaches to new business problems or use cases.

Role definition

Data Science

Primary driver of analytic project and result Lead code reviews, final approvers of models before reaching customer, set high-bar for model development

Evaluate and create new frameworks and define methodologies/ governance

Scope & key responsibility

Manage small-medium sized teams (direct or indirect)

External hire: Preferred Academic Background and Professional Experience

At least a Bachelor degree and 10+ years of relevant work experience. Or a Masters Degree in a relevant field and 5+ years of relevant work experience. Or a PhD in a relevant field with 2+ years of relevant work experience.