{{Company name}} is hiring a Data Scientist to move us from reporting what happened to predicting what will happen — and proving what works. You will design experiments, build models that ship, and help teams make decisions grounded in evidence rather than instinct.

What you'll do

• Frame business problems as testable hypotheses and choose the simplest method that answers them.
• Design, run, and analyze experiments, including power analysis and honest reads on inconclusive results.
• Build predictive and statistical models — from regression baselines to machine learning where it earns its keep.
• Work with engineering to get models into production and monitor them once they are there.
• Explore new data sources and build features that improve model performance.
• Present findings to non-technical stakeholders with clear assumptions and caveats.
• Raise the analytical bar across the company through reviews, documentation, and mentoring.

What we're looking for

• {{Years of experience}} years applying statistics or machine learning to real business problems.
• Strong Python for data work (pandas, scikit-learn or equivalent) and confident SQL.
• Solid grounding in statistics: inference, experimental design, and the failure modes of both.
• Experience taking at least one model or analysis from idea to measurable business impact.
• Judgment about when a heuristic beats a model — and the honesty to say so.
• Clear communication of technical work to people who will never read the notebook.

Nice to have

• Experience with causal inference methods beyond A/B testing.
• Familiarity with modern ML tooling for training, tracking, and deployment.
• Publications, talks, or open-source work in your area.
• Domain experience in {{Industry}}.

Compensation and benefits

• {{Salary range}}
• {{Benefits summary}}
• {{Location / remote policy}}

How to apply

{{Application instructions}} Tell us about one project where your analysis or model changed what the business did — and what you would do differently now.