Data Scientist

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

Job / Role Description

• 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

Expected Outcome / What Success Looks Like

• 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