Applied Data Scientist

About The Role

The role designs statistical and machine learning solutions that translate messy, high‑dimensional data into clear business insight. The work spans the full spectrum: you will write production SQL one day and design a causal inference study the next. Scientific rigor and business impact matter in equal measure.

• Develop, validate, and deploy predictive models (regression, classification, clustering, time‑series) for real business decisions across client verticals
• Design and analyze A/B tests and quasi‑experimental studies with appropriate statistical rigor; communicate results to business and executive stakeholders
• Write efficient, well‑documented SQL and Python analytical code; maintain model pipelines with appropriate quality monitoring
• Collaborate with data engineers on feature engineering, data quality, and pipeline reliability for training and serving
• Conduct exploratory data analysis to surface non‑obvious patterns and generate hypotheses that drive product roadmap decisions
• Present findings clearly in written reports, dashboards, and executive presentations ‑ translating statistical nuance into confident recommendations
• Contribute to team knowledge‑sharing; stay current on methodological advances relevant to our problem spaces

• 2–5 years of data science experience with demonstrable production impact (not just analyses ‑ decisions that changed what someone did)
• Strong Python: pandas, scikit‑learn, stats models; SQL at the level of writing and optimizing complex analytical queries without help
• Deep statistical foundations: hypothesis testing, regression modeling, experimental design, probability distributions
• Experience with at least one cloud data warehouse:
  Snowflake, Big Query, or Redshift
• Clear, structured written and verbal communication ‑ you can make a p‑value meaningful to a CFO
• MS or BS in Statistics, Computer Science, Mathematics, Economics, or a closely related quantitative field
• Bonus: causal inference methods (DiD, synthetic control, IV), ML model deployment experience, Spark, or NLP

Boston, MA (Hybrid)

• New York City
• San Francisco
• Seattle