Data Scientist – Machine Learning

About Aarvian Aarvian is a 'Process-First' AI transformation partner dedicated to operationalizing Data, ML, and AI for the global enterprise. We move beyond fragmented tools by aligning intelligence with business logic — mapping solutions from Process to Function, and ultimately to the Business Unit's P&L. Our work is anchored on four core pillars: • Domain-led Expertise — deep industry knowledge that grounds every AI solution in real-world business context. • Comprehensive Lifecycle Ownership — end-to-end engagement from Advisory through to Adoption. • Co-development Trust — we build with our clients, not just for them, fostering shared accountability. • Ethical AI Foundation — a steadfast commitment to responsible, transparent, and fair AI practices. At Aarvian, you will work alongside practitioners who bring rigorous thinking and genuine domain depth to some of the most complex AI challenges in global enterprise retail.

We are looking for an experienced Data Scientist with a strong machine learning background and hands-on experience in the retail domain. The ideal candidate will have a proven track record of building and deploying ML models that drive measurable business outcomes — including demand forecasting, pricing optimization, and customer analytics. You will work closely with business, engineering, and product teams to deliver data-driven solutions at scale.

Demand Forecasting & Time Series Analysis • Design and develop demand forecasting models using time series techniques such as ARIMA, SARIMA, Prophet, LSTM, and Temporal Fusion Transformers. • Incorporate external signals (promotions, seasonality, holidays, market trends) to improve forecast accuracy. • Build and maintain automated forecasting pipelines for SKU-level and category-level demand planning. • Collaborate with supply chain and inventory teams to translate forecasts into actionable replenishment decisions. Price Elasticity & Pricing Analytics • Develop price elasticity models to quantify the sensitivity of consumer demand to price changes across product categories. • Support dynamic pricing strategies by building models that recommend optimal price points to maximize revenue and margins. • Conduct promotional lift analysis and markdown optimization using regression and causal inference techniques. • Collaborate with the commercial and category management teams to embed pricing insights into business workflows. Customer Analytics • Build customer segmentation models (RFM, clustering, propensity models) to enable targeted marketing and personalization. • Develop churn prediction and customer lifetime value (CLV) models to support retention strategies. • Design and analyze A/B experiments to evaluate the impact of marketing campaigns and product changes. • Build recommendation systems to improve cross-sell, upsell, and product discovery experiences. General Data Science & MLOps • Own the end-to-end model lifecycle — from ideation and experimentation to deployment, monitoring, and retraining. • Ensure model performance, fairness, and explainability across production systems. • Work with data engineers to define feature pipelines and maintain high data quality standards. • Communicate findings and model insights to both technical and non-technical stakeholders through clear visualizations and presentations.