Mayank Ahuja

• Lead Data Science initiatives for promotion product suite.
• Build scalable applications by partnering with Engineering and Product team.
• Work with product management to define and continuously refine product roadmap based on priorties.
• Mentor, guide data scientists to solve complex business problems faced by during development.
• Translate and explain complex machine learning models to the Executive team.
• Close to million ML models are now in production across different retailers that provides baselines, promotion uplifts, cannibalization and pantry loading impacts of promotion.
• Involved in the data science development of cloud based (Azure) RGM tool to enable CPG for planning their trade promotions. This tool provides the flexibility of optimizing on various metric's for CPG as well as their Retailers while ensuring all the relevant business realities are taking into consideration. Successful POCs across US and Australian market.

Some of the projects i worked/lead

• Leading Food and beverage company – Trade Promotion optimization - Project scope involved identifying trade spend optimization opportunities in 6 brands at 2 retailers. ROI for each promotion was computed through statistical modeling and then optimization model was used to determine the optimal re-allocation of trade spends while considering business constraints.
• Global Personal care products manufacturer – Baseline forecasting - Project scope involves generating 1 year ahead baseline forecasts for Infant care, Feminine care , Family care PPGs across all customers/retailers for 10 countries in LAO. Statistical models were built at PPG X Planning node level to decompose the units into baseline and incremental.
• Leading Direct selling company of Beauty and Fashion products – Revenue management - Client's pricing in the market was not competitive and they were struggling with declining margins and low RoI on discounts on trade promotions. We undertook a revenue management exercise and evaluated competitive pricing, brand positioning, portfolio, profitability, ROI of trade promotions through an extensive analytics exercise.
• Leading Snack food brand – Trade Promotion event simulation - Project scope involved helping KAM of Snack category to identify new deals that can sit on top of the promotion calendar. Econometric modeling was done at PPG X Store cluster level to decompose the historical volumes into different promotion attributes such as price discount, Store display (End cap , Perimeter etc.), Feature AD (Front page / Back page).
• Leading Mexico retailer – Promotion effectiveness/forecasting - Project scope involved help building promotional calendar with buyers for health and beauty department on a bi-weekly basis. Key steps involved were – Data collection and processing at item store level (1500 items across 411 stores) , Econometric modeling, Feeding the results into a simulation tool which buyer can use.
• Leading UK mobile handset retailer : Pricing and Forecasting – Project scope involves setting prices for both prepay and post pay business segment at a handset – network level (prepay) and handset – network – tariff code level (post pay). Pricing elasticity models were build to capture own price, cross price and competitor impact.