Data Scientist - Azure & Machine Learning

As Data Scientist, you will spearhead the end-to-end development of sales forecasting and demand sensing models for CPG portfolios on Databricks (Azure). Work closely with commercial, supply chain, and engineering teams to build ML solutions that improve forecast accuracy, reduce inventory waste, and support revenue growth. Bring deep ML expertise, strong Python engineering skills, and a nuanced understanding of CPG market dynamics and you are comfortable translating complex model outputs into clear business recommendations.

Skills / Experience

• 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)

Secondary Skills / Good to have

• SQL & Data Engineering Fundamentals - Advanced SQL on Delta Lake / Azure Synapse; ability to build lightweight feature pipelines without full data engineering support
• MLOps & CI/CD for ML - MLflow, GitHub Actions, or Azure DevOps pipelines to automate model retraining, evaluation gates, and deployment to Databricks Model Serving.
• Data Visualisation & Storytelling - Power BI, Plotly, or Streamlit dashboards to communicate forecast accuracy and business KPIs to non-technical stakeholders.
• Promotional & Trade Analytics - Modelling promotional uplift, baseline vs incremental volume splits, and trade spend ROI key for CPG forecast decomposition
• Team Leadership & Mentoring - Guide junior data scientists, run code reviews, define modelling standards, and represent the data science function in cross-functional forums