This free Data Scientist job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring someone to apply statistics and machine learning to your data — building models, running experiments, and turning data into predictions or product features. It assumes a true data science role, not analytics or pure engineering.
Data science means very different things across companies, so be specific about whether the role leans toward analysis and experimentation, product-facing modeling, or research. Candidates calibrate hard on this.
If the role is mainly dashboards and business questions, use the Data Analyst template; if it's productionizing models at scale, use the Machine Learning Engineer template.
Writing tips
- Clarify the focus: experimentation and analysis, product modeling, or research.
- Name the problems they'll work on; vague 'data science' roles attract a flood of mismatches.
- Be clear about how much production engineering vs. analysis the role involves.
- Distinguish from Data Analyst and Machine Learning Engineer roles.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. We have rich data and real problems to solve with it, and we're hiring a Data Scientist to help us learn faster and build smarter.
The role
As a Data Scientist, you'll use statistics and machine learning to answer hard questions and build data-driven features. You'll frame problems, build and validate models, design experiments, and turn results into decisions and product improvements. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Frame business and product problems as data science problems.
- Build, validate, and iterate on statistical and machine learning models.
- Design and analyze experiments (A/B tests and beyond).
- Turn findings into clear recommendations and, where relevant, product features.
- Partner with engineering to take promising models toward production.
What we're looking for
- 3+ years in a data science role with a track record of impact.
- Strong foundations in statistics, experimentation, and machine learning.
- Proficiency in [Python / R] and SQL.
- The judgment to choose simple methods when they're the right ones.
- Excellent communication — you can explain models and results to non-experts.
Nice to have
- Experience taking models into production.
- Domain experience in [your area].
- Familiarity with modern ML tooling and cloud platforms.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- Real problems, rich data, and the autonomy to make an impact.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does a Data Scientist do?
- A Data Scientist uses statistics and machine learning to answer questions and build data-driven features. They frame problems, build and validate models, design and analyze experiments, and translate results into decisions and product improvements.
- What's the difference between a Data Scientist and a Data Analyst?
- A Data Analyst focuses on describing what's happening through reporting and analysis. A Data Scientist leans more toward prediction and modeling — statistics, machine learning, and experimentation. Analysts are typically closer to the business; scientists closer to algorithms, though the roles overlap.
- What's the difference between a Data Scientist and a Machine Learning Engineer?
- A Data Scientist builds and validates models and focuses on insight and experimentation. A Machine Learning Engineer focuses on deploying and operating models reliably in production. Data science answers 'does this model work?'; ML engineering answers 'how do we run it at scale?'