Data Engineer

• 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.

• 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/Google Cloud Platform) 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., * 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.

iSoftStone is a global IT service and consulting company that creates value and drives success through technology solutions, service excellence, and digital innovation. We specialize in web and application development, software testing and support, data and content management, digital experience, accessibility, and data for machine learning and AI. With 20 delivery centers and more than 90,000 employees worldwide, iSoftStone is proud to serve some of the world’s most well-known businesses, including 90+ Fortune Global 500 companies.