DescriptionThis role is a 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.
Qualifications:
- Five+ years 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 - we specifically value candidates with 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.
Pay rate: $130,000 to $150,000/year
*This is a Hybrid Role in the New York City Metro Area*
**Client Site Travel Required - Up to 25%**
***Candidates must have permanent authorization to work in the United States. Visa sponsorship is not available for this role.***
