| Detail | For this role |
|---|---|
| Department | Data and Analytics |
| Level | Senior management |
| Reports to | Head of Data |
| Direct reports | None |
| Experience | 10+ years in data science at a deep technical level |
Principal Data Scientist job description template
Copy this job description, replace the text in square brackets and post it on your careers page or a job portal.
Job title: Principal Data Scientist
Department: Data and Analytics
Reports to: Head of Data
Location: [City], [office, branch or site]
About the role
A Principal Data Scientist is the top individual contributor in data science. They tackle the hardest problems, set the technical direction for modelling across teams, and raise the bar on rigour and impact. The role is senior and deeply technical, without necessarily managing people. A good Principal Data Scientist solves problems others cannot, sets methods and standards the whole function follows, guides the toughest technical decisions, and turns advanced modelling into durable business advantage.
Key responsibilities
- Solve the hardest and highest value data science problems.
- Set the technical direction and methods for modelling across teams.
- Raise the standard of rigour, evaluation and reproducibility everywhere.
- Advise leadership on where data science can create advantage.
- Guide the toughest technical and modelling decisions across teams.
- Mentor senior data scientists and leads on method and craft.
- Prototype advanced approaches and prove their value with data.
- Represent data science in the cross functional technical forums.
- Keep the function current on the state of the field.
Requirements
- Master's or PhD in a quantitative field
- Exceptional record in applied data science
- Deep expertise in a modelling domain
- 10+ years in data science at a deep technical level
KRAs and KPIs for a Principal 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 |
|---|---|
| Hard problems | High value, hard problems solved that others could not |
| Technical direction | Methods and standards adopted across data science teams |
| Impact | Advanced models delivering durable business advantage |
| Rigour | Evaluation and reproducibility standards raised and held |
| Influence | Leadership decisions informed by sound data science advice |
| Mentoring | Senior scientists improving in method and craft |
Skills and tools
Tools used day to day: Python, PyTorch or TensorFlow, SQL, MLflow and notebooks, Cloud ML platforms.
Reporting line and career path
Next roles: Distinguished Data Scientist, Head of Data Science, Chief Data Scientist
Interview questions for a Principal Data Scientist
- Tell me about the hardest data science problem you have solved.
- How do you set modelling standards that other teams will adopt?
- How do you decide when an advanced method is worth the complexity?
- How do you prove the value of a new approach to leadership?
- How do you keep evaluation honest on a high stakes model?
- How do you mentor senior scientists without managing them?
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What does a Principal Data Scientist do?
A Principal Data Scientist is the top individual contributor in data science. They solve the hardest, highest value problems, set modelling methods and standards across teams, advise leadership, and mentor senior scientists. They may not manage people. Their job is turning advanced modelling into durable business advantage and raising the whole function.
What is the difference between a Lead and a Principal Data Scientist?
A Lead Data Scientist leads a team and its projects. A Principal Data Scientist is a senior individual contributor who sets technical direction across teams and tackles the hardest problems, usually without managing people. The lead owns a team; the principal owns technical depth and influence across the function.
What qualifications does a Principal Data Scientist need?
Often a master's or PhD in a quantitative field, with an exceptional record in applied data science and deep expertise in a modelling domain. Employers value the ability to solve problems others cannot, set standards others adopt, and influence both engineers and leadership.