Owned the data-science workstream of a Strategic Revenue Management (SRM) & Trade Promotion Optimisation platform for a US consumer-goods business, from demand modelling through to an optimiser and Power BI reporting used by SRM and account teams.

• Developed a sales-portfolio analytics & reporting platform in Power BI on Azure SQL for the entire US billion-dollar market, giving teams a single source of truth to steer trade investment.
• Implemented Marketing Mix Modelling (MMM) techniques on sales, pricing, promotion and marketing data to analyse strategy effectiveness and optimise marketing-spend allocation.
• Built an end-to-end data-science workflow and model datasets for log-log regression demand models, validated out-of-sample with R², MAPE and economically-valid elasticities over 3 years of weekly data using Python, PySpark, SQL and R.
• Designed and deployed the end-to-end ML lifecycle on Azure Databricks, Data Lake, SQL, Azure ML, DevOps and Git into production.
• Created trade-promotion optimisation tools across 100+ markets, improving profit margins by 5–8% on optimised plans through strategic trade-merchandise planning.
• Collaborated with cross-functional teams (data scientists, engineers, product owners, analysts) to define requirements, validate model outputs and translate results into business insight.

Built customer-intelligence and segmentation models to drive smarter debt-recovery and targeting strategies, with executive dashboards to support decisions.

• Created an RFM model in Python to segment 200K+ customers by payment behaviour and outstanding debt into high-, medium- and low-risk profiles.
• Enabled targeted debt-recovery strategies by aligning resource allocation to risk tiers, increasing recovery rates by 14% and reducing average recovery time by 12 days.
• Wrote complex SQL queries and Python scripts for data cleansing, manipulation and analysis on large datasets.
• Prepared executive-level Power BI and Tableau dashboards visualising customer segments, transactions, debt ageing, exposure and recovery status.

Analysed large-scale insurance data to surface operational insights, SLA breaches and claims patterns, and built monitoring dashboards for operations leadership.

• Processed and analysed 1M+ claims, policies and provider records using SQL, Power BI and Excel to identify SLA breaches, claim success/failure rates, risk patterns and payment delays.
• Implemented Power BI dashboards to monitor real-time claim risks, adjuster performance and resolution bottlenecks, improving process visibility.
• Collaborated with data scientists, product owners, business analysts and operations teams to present insights and support backlog refinement and reporting.