| Detail | For this role |
|---|---|
| Department | Data and Analytics |
| Level | Senior management |
| Reports to | Data Science Manager |
| Direct reports | Data Scientist, Machine Learning Engineer |
| Experience | 7 to 10 years in data science with team leadership |
Lead Data Scientist job description template
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Job title: Lead Data Scientist
Department: Data and Analytics
Reports to: Data Science Manager
Location: [City], [office, branch or site]
About the role
A Lead Data Scientist leads a team of data scientists on high value machine learning and analytics problems. They frame problems, guide modelling approaches, review work, and make sure models are sound and make it into production. The role blends deep technical skill with leadership. A good Lead Data Scientist picks the problems worth solving, keeps modelling rigorous and honest, mentors the team, and turns models into results the business can measure and rely on.
Key responsibilities
- Frame business problems as data science and machine learning tasks.
- Lead the design of models, features and evaluation approaches.
- Review the team modelling work for rigour and correctness.
- Guide the path from model to production with engineering.
- Set the standards for experiments, validation and documentation across the team.
- Mentor the data scientists and grow their technical skill.
- Measure model impact against the business metrics after launch.
- Manage stakeholders on what data science can and cannot do.
- Keep the team current on methods and tools that add value.
Requirements
- Master's or bachelor's degree in a quantitative field
- Strong record of production data science
- PhD is an advantage
- 7 to 10 years in data science with team leadership
KRAs and KPIs for a Lead Data Scientist
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 |
| Modelling rigour | Models validated properly with no leakage or overfitting missed |
| Delivery | Priority projects delivered to production on plan |
| Team growth | Data scientists improving on skills and independence |
| Reproducibility | Experiments and models documented and reproducible |
| Stakeholder trust | Stakeholders clear on model use, limits and results |
Skills and tools
Tools used day to day: Python, SQL, scikit-learn and PyTorch, Notebooks and MLflow, Cloud ML platforms.
Reporting line and career path
Next roles: Data Science Manager, Principal Data Scientist, Head of Data Science
Interview questions for a Lead Data Scientist
- How do you decide which data science problems are worth solving?
- How do you review a model for leakage and overfitting?
- How do you get a model from notebook to production?
- How do you measure the business impact of a model?
- How do you mentor a data scientist who overcomplicates models?
- How do you set stakeholder expectations on a model?
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What does a Lead Data Scientist do?
A Lead Data Scientist leads a team on high value machine learning and analytics problems. They frame problems, guide modelling, review work for rigour, and drive models into production with engineering. They mentor the team and measure impact. Their job is sound models that deliver real, measured business value.
What is the difference between a Lead Data Scientist and a Data Science Manager?
A Lead Data Scientist is the senior technical lead: problem framing, modelling direction and review, and mentoring. A Data Science Manager focuses more on people, planning, priorities and stakeholders. The lead owns the science; the manager owns the team and delivery. Roles overlap in smaller teams.
What qualifications does a Lead Data Scientist need?
A degree in a quantitative field, often a master's or PhD, with a strong record of production data science. Employers value deep machine learning and statistics, Python and SQL, sound model evaluation, and the ability to lead and mentor a team and communicate with stakeholders.