Senior Data Analyst
$130,000–$150,000 year
HybridNew York City, New York, United States or New York, United States
Job Summary
Own end-to-end delivery on retail analytics engagements, including discovery, data assessment, feature design, modeling, validation, deployment handoff, and results readout. Build and productionize models across the retail value chain for demand forecasting, inventory optimization, customer segmentation, and price/promo elasticity. Design data models and semantic layers on client platforms like Snowflake and Databricks, interrogating data quality and business logic before modeling. Present findings to director- and VP-level stakeholders, quantifying business impact in margin and working capital terms. Support pre-sales activities including solution shaping, estimation, and POC design. Mentor junior analysts and contribute reusable accelerators to the retail practice. This client-facing role requires up to 25% client site travel and permanent US work authorization.
Required Qualifications
- Permanent authorization to work in the United States
- Visa sponsorship is not available
- Client Site Travel Required – Up to 25%
- Five+ years applied data science experience
- Meaningful time on retail, CPG, or e-commerce problems
- Strong data modeling fundamentals — dimensional modeling, star/snowflake schemas, slowly changing dimensions, grain definition
- Ability to look at a retail transaction feed and design the model, not just query it
- Advanced SQL
- Production-grade Python (pandas, scikit-learn, statsmodels)
- Comfort with at least one of PyTorch/TensorFlow, Prophet/ARIMA-family forecasting, or causal inference frameworks
- Demonstrated experience with time series forecasting and/or econometric modeling (elasticity, uplift, incrementality)
- Cloud data platform experience (Snowflake, Databricks, Azure/AWS/GCP)
- Familiarity with CI/CD and version control practice
- Ability to work directly with clients: run a working session, handle pushback on methodology, and write a deck that a merchant will actually read
- Bachelor's degree in a quantitative discipline
Desired Qualifications
- Mathematics, Statistics, or Operations Research major
- Formal mathematical training and the ability to reason from first principles about optimization, probability, and model assumptions
- Advanced degree (MS/PhD) in a quantitative field
- Retail domain knowledge: open-to-buy, allocation, replenishment, size/pack optimization, omnichannel inventory, RFM and loyalty analytics
- LLM/GenAI application experience in a retail context (demand sensing, agentic workflows, unstructured product or review data)
- Consulting or professional services background
- Experience with retail systems data a plus— SAP, Salesforce Commerce Cloud, O9
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