| Detail | For this role |
|---|---|
| Department | Data and Analytics |
| Level | Manager |
| Reports to | Head of Data |
| Direct reports | Lead Data Scientist, Data Scientist, Machine Learning Engineer |
| Experience | 8 to 12 years in data science with 2 years leading a team |
Data Science Manager job description template
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Job title: Data Science Manager
Department: Data and Analytics
Reports to: Head of Data
Location: [City], [office, branch or site]
About the role
A Data Science Manager leads a team of data scientists delivering machine learning and advanced analytics for the business. They pick the problems, plan the work, manage the people, and make sure models reach production and create value. The role balances people leadership with technical judgement. A good Data Science Manager aligns the team to high value problems, keeps modelling rigorous, gets models into production with engineering, and grows data scientists into strong, reliable contributors.
Key responsibilities
- Lead the data science team and its roadmap of projects.
- Choose high value problems worth solving with data science.
- Plan and prioritise the work against business value and capacity.
- Guide modelling approaches and review work for rigour with leads.
- Drive models into production and measure their business impact.
- Manage stakeholders on what data science can realistically deliver.
- Set clear standards for experiments, validation and model reproducibility.
- Partner with data engineering on data and deployment needs.
- Hire, mentor and retain data scientists on the team.
Requirements
- Master's or bachelor's degree in a quantitative field
- Strong applied data science experience
- Some team leadership experience
- 8 to 12 years in data science with 2 years leading a team
KRAs and KPIs for a Data Science Manager
Key result areas for the appraisal form, each with a KPI you can measure every month or quarter.
| Key result area | How to measure it |
|---|---|
| Business impact | Models in production delivering measured business value |
| Delivery | Priority projects delivered to production on plan |
| Modelling rigour | Models validated properly before deployment |
| Stakeholder trust | Stakeholders clear on model use, limits and results |
| Reproducibility | Experiments and models documented and reproducible |
| Team | Data scientists retained and growing in capability |
Skills and tools
Tools used day to day: Python, SQL, scikit-learn and PyTorch, MLflow, Cloud ML platforms.
Reporting line and career path
Next roles: Head of Data Science, Head of Data, Director of Data Science
Interview questions for a Data Science Manager
- How do you choose which data science problems the team works on?
- How do you get models out of notebooks and into production?
- How do you keep modelling rigorous across the team?
- How do you set stakeholder expectations on a model?
- How do you measure the business value of your team work?
- How do you develop and retain strong data scientists?
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What does a Data Science Manager do?
A Data Science Manager leads a team delivering machine learning and advanced analytics. They pick high value problems, plan the work, manage the people, guide modelling with leads, and drive models into production. They manage stakeholders and measure impact. Their job is data science that reaches production and creates real value.
What is the difference between a Data Science Manager and a Lead Data Scientist?
A Lead Data Scientist is the senior technical lead: framing problems, guiding modelling and reviewing work. A Data Science Manager focuses more on people, planning, priorities and stakeholders. The lead owns the science; the manager owns the team and delivery. In small teams one person does both.
What qualifications does a Data Science Manager need?
A degree in a quantitative field with strong applied data science experience and some team leadership. Employers value machine learning judgement, problem selection, the ability to get models into production, stakeholder management, and a record of building and retaining data science teams.