Senior Data Scientist

This is a hybrid role 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

This is a client-facing data scientist role 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/year