About the Data Scientist role
A Data Scientist uses statistics, machine learning, and domain understanding to answer hard questions and build predictive capabilities: from churn models and forecasting to experimentation design and recommendation systems.
The role varies with data maturity. In some companies, data scientists spend most of their time on analysis and experimentation; in others, they build and ship production models alongside ML engineers. Be explicit about your infrastructure, the problems on the roadmap, and how models reach production, because candidates burned by 'data science' roles that turned out to be dashboard-building will check carefully.
Look for scientific honesty above all: candidates who quantify uncertainty, report negative results, and choose simple baselines before reaching for complex models tend to create real value.
Data Scientist job description template
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Job brief
Our product generates more data than we currently know what to do with, and the questions piling up are getting expensive to leave unanswered. As our Data Scientist, you will change that: digging into large datasets, shipping predictive models where they earn their keep, and designing experiments rigorous enough that the whole company trusts the results.
You will partner with product managers on what to measure, with engineers on how models reach production, and with leadership on what the findings mean for strategy. Strong statistical instincts matter here, but so does judgment: knowing when a regression beats a neural network, and when a well-framed chart beats both.
Responsibilities
- Frame open-ended business questions as tractable analytical or modeling problems
- Dig through large datasets to surface trends, patterns, and opportunities others have missed
- Develop and validate predictive models, choosing techniques that fit the problem rather than the hype cycle
- Design A/B tests and other experiments, then evaluate them with proper statistical rigor
- Turn analyses and models into concrete recommendations that product and business teams can act on
- Translate complex results into plain language for non-technical stakeholders
- Hunt down valuable data sources and work with engineering to make them clean and usable
- Ship models to production together with engineers and keep an eye on their behavior once live
- Define the metrics that measure model and initiative impact, and track them honestly
- Write up methods and results so any teammate can reproduce the work
- Watch for model drift, data quality issues, and bias, and raise them before they cause damage
Requirements and skills
- A body of shipped data science work: models built, experiments run, and decisions influenced
- Deep statistical grounding across hypothesis testing, regression, and experimental design
- Practical machine learning experience, including a feel for each technique's trade-offs
- Fluent Python or R for modeling, plus solid SQL for getting at the data
- Comfort with core data science libraries such as pandas, scikit-learn, or equivalents
- Business sense for picking problems where a model actually moves a metric
- The ability to present findings so that a room of non-specialists leaves convinced and correct
- A BSc or MSc in Computer Science, Statistics, Mathematics, or another quantitative field
Nice to have
- Hands-on experience deploying models to production and monitoring them
- Deep learning framework experience such as PyTorch or TensorFlow
- Familiarity with modern data stacks: dbt and warehouses such as BigQuery or Snowflake
- Domain knowledge in our industry
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