Data, Research & Analytics
Animated publication
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.
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