Data Scientist Job Description

Key skills

Responsibilities

• • Frame ambiguous business questions as well-scoped analytical or modeling problems
• • Design and analyze A/B experiments from power calculation through post-experiment interpretation
• • Build, evaluate, and document predictive models for business-critical decisions
• • Collaborate with data engineers to ensure clean, reliable data pipelines for modeling work
• • Deliver clear, actionable insights to product and business stakeholders in non-technical language
• • Monitor deployed models for performance degradation and concept drift
• • Contribute to a shared library of analytical frameworks and reusable notebooks
• • Identify new opportunities where data-driven approaches can create measurable business impact

Requirements

• • 3+ years of applied data science experience in a product or business context
• • Strong statistical foundations: distributions, estimation, significance testing, and Bayesian reasoning
• • Proven ability to run end-to-end A/B experiments with proper controls
• • Hands-on experience building and deploying at least one production predictive model
• • Proficiency in Python and SQL for data manipulation and exploratory analysis
• • Demonstrated ability to communicate technical findings clearly to non-technical audiences

Nice to have

• • Experience with causal inference methods such as diff-in-diff, instrumental variables, or regression discontinuity
• • Familiarity with ML experiment tracking tools (MLflow, Weights & Biases)
• • Background in a quantitative academic discipline (statistics, econometrics, physics, etc.)
• • Exposure to NLP or time-series forecasting in a production context
• • Experience designing and analysing a controlled experiment whose result directly changed a product or business decision
• • Comfort explaining a model's assumptions and limitations to a non-technical audience without overclaiming

What to look for in a great Data Scientist

Strong data scientists ask hard questions before building anything. In interviews, probe whether candidates push back on poorly specified problems — if they immediately jump to model selection without questioning the objective, that is a red flag. Look for statistical rigor: can they explain why their A/B test was powered correctly, or what a p-value actually means? Business impact orientation matters greatly: the best candidates describe their work in terms of decisions changed or outcomes improved, not models trained. Communication skill is equally non-negotiable — if insights cannot be understood by stakeholders, they will not drive action.

Interview questions to ask a Data Scientist

Present a realistic but ambiguous business question — for example, 'our churn rate increased last quarter, why?' — and observe how the candidate structures an analytical approach. Strong candidates will ask clarifying questions, propose multiple hypotheses, and describe what data they would need. Include an experiment design exercise: ask them to design an A/B test for a product change, covering sample size, guardrail metrics, and interpretation of inconclusive results. Ask about a model they built that did not work as expected and what they learned. Avoid questions that reward memorized algorithms over genuine statistical reasoning.

Where to source Data Scientists

Kaggle competition leaderboards surface practitioners who are comfortable with competitive modeling, though strong Kaggle performance does not always correlate with business impact. Academic networks — especially master's and PhD programs in statistics, applied mathematics, and computer science — are productive for candidates with rigorous foundations. LinkedIn with filters on specific methods (causal inference, Bayesian, time series) helps identify specialists. Data science communities on Slack (e.g., Locally Optimistic for analytics-oriented practitioners) and conference networks such as PyData and StanCon surface thoughtful practitioners who engage with the field seriously.

Red flags when hiring a Data Scientist

The biggest risk is a candidate who reaches for a complex model when the problem needs a clear metric and a simple baseline. Watch for people who quote accuracy without asking about class imbalance, or who cannot explain how their work changed a decision. Be wary of those who never mention data quality, leakage, or how they validated results; impressive notebooks mean little if the conclusion doesn't hold. Another flag is treating statistics as an afterthought behind machine learning. Ask about a project where the data was messy or the answer was inconvenient, and what they did. Candidates who talk only about algorithms, never about the business question or the assumptions behind their model, usually struggle to deliver anything a stakeholder can act on.

How an ATS speeds up hiring a Data Scientist

Data science roles draw a wide mix of backgrounds, from statistics to software, so screening is where time gets lost. With Pitch N Hire you can post the role across boards and route applicants through a structured screen that captures the specifics you care about, such as experimentation experience or a portfolio link, before a reviewer invests time. A shared scorecard lets the panel rate the same competencies (statistical reasoning, coding, communication, business framing) consistently, which matters when candidates come from very different disciplines. Take-home analyses and their write-ups attach to the candidate record, so reviewers judge the reasoning, not just the result, and the hiring manager sees every candidate's strengths in one view rather than piecing feedback together from scattered notes.