Data Scientist

• 8 years in data science or Applied ML roles; 3 years of experience in Databricks in production
• 5 years of experience in Python — Pandas, PySpark, scikit-learn, Azure ML or Azure ecosystem and Databricks experience in production
• 5 years of experience in Supervised, unsupervised Machine Learning (ML) algorithms, forecasting and inventory optimization
• 5 years of experience in deep learning algorithms applying to solve forecasting, regression and classification problems
• 3 years of experience in building ML models in CPG, FMCG, or Retail analytics industry
• 3 years of experience in MLflow or equivalent experiment tracking tool 
• Master's or PhD in Statistics, CS, or related field (preferred)

• Lead end-to-end sales forecasting model development — from data sourcing and feature engineering through model training, validation, and productionisation on Databricks (Azure)
• Design and maintain forecasting pipelines — at SKU, category, and regional hierarchy levels — incorporating POS data, promotional calendars, seasonality indices, and external signals (macroeconomic, weather)
• Apply CPG domain knowledge — to model promotional uplift, new product introduction curves, product cannibalization, and retailer sell-in/sell-out dynamics into ML features and targets
• Operationalise ML models using MLflow on Databricks — manage the model registry, version control experiments, automate retraining schedules, and configure drift monitoring alerts
• Collaborate with commercial and supply chain teams — to translate forecast outputs into inventory recommendations, production planning inputs, and revenue growth strategies
• Define and enforce data science best practices — modelling standards, experiment documentation, code review guidelines, and reproducibility requirements across the team
• Mentor junior data scientists — conduct code reviews, lead knowledge-sharing sessions, support career development, and build a high-performance data science culture
• Communicate model insights and forecast accuracy — to senior stakeholders through dashboards, executive briefings, and written reports — making complex model behaviour accessible to business audiences
• Drive continuous model improvement — benchmark new algorithms, evaluate AutoML approaches, and run controlled experiments to improve MAPE, bias, and coverage metrics
• Partner with data and platform engineers — to ensure feature pipelines on Azure Data Lake / Delta Lake are reliable, scalable, and aligned with model refresh cadence requirements
• Communicate effectively with internal and customer stakeholders; Strong interpersonal skills to build and maintain productive relationships with team members
• Problem-Solving and Analytical Thinking; Capability to troubleshoot and resolve issues efficiently
• Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps
• Provides regular updates, proactive and due diligent to carry out responsibilities

• Data Scientist is expected to meet customer expectations within accelerated timelines, enabling us to strengthen our capabilities and drive growth in this area
• Statistical Analysis & Experimentation - A/B testing, causal inference, and hypothesis testing to measure the business impact of model improvements and pricing interventions
• This role offers the opportunity to lead high-impact data science initiatives that directly shape customer outcomes and gain strong visibility with senior leadership

Salary : $130,000 - $140,000