Senior Data Scientist

This is a hybrid role based in the New York City metro area with up to 25% client-site travel required. Candidates must have permanent authorization to work in the United States; visa sponsorship is not available.

About the Role

Client-facing data scientist supporting enterprise retail accounts across merchandising, supply chain, customer, and pricing analytics. You'll work embedded with client teams — scoping the problem, building the model, and defending the methodology to business stakeholders who are not data people. This is a consulting role: billable, multi-account, and dependent on your ability to translate ambiguous business questions into tractable modeling problems.

Responsibilities

• Own end-to-end delivery on retail analytics engagements: discovery, data assessment, feature design, modeling, validation, deployment handoff, and results readout.
• Build and productionize models across the retail value chain — demand forecasting and inventory optimization, customer segmentation and CLV, price/promo elasticity and markdown optimization, assortment and allocation.
• Design data models and semantic layers on client data platforms (Snowflake, Databricks, Fabric, BigQuery); work with data engineering to define the tables the models actually need rather than accepting what exists.
• Interrogate data quality and business logic before modeling — retail data is messy, and identifying the flaw in a returns table or a channel attribution rule is often worth more than a better algorithm.
• Present findings to director- and VP-level client stakeholders; quantify business impact in margin, sell-through, GMROI, or working capital terms, not model metrics.
• Support pre-sales: solution shaping, estimation, POC design, and technical credibility in client pitches.
• Mentor junior analysts and contribute reusable accelerators to the retail practice.

Requirements

• Five or more years of applied data science experience, with meaningful time on retail, CPG, or e-commerce problems.
• Strong data modeling fundamentals — dimensional modeling, star/snowflake schemas, slowly changing dimensions, grain definition. You should be able to look at a retail transaction feed and design the model, not just query it.
• Advanced SQL and 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) and 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.

Preferred 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 (SAP, Salesforce Commerce Cloud, O9).

Pay

$130,000 to $150,000 per year.