PepsiCo - Manager - Data Science

Functional Responsibilities:

- Support delivery of descriptive and predictive analytics as defined by the SLA (Service Level Agreement) within the AMESA Business Service Service Center

- Execute deep descriptive analytics of business performance and drivers to supplement standard reporting and inform data-driven decisions

- Identify, assess, and visualize key market share drivers for AMESA Categories, as a growth catalyst to prioritize and enable brand planning across portfolio

- Support AMESA region's annual SKU optimization process for the portfolio; analyzing impact by channel, customers and region as needed based on HQ delivered recommendations & targets

- Advise and share Advanced Analytics models and best practices with Regional Category Managers to leverage and build Advanced Analytics capability.

- Develop, maintain, and apply statistical models to business questions - including forecasting, price sensitivities/corridors, drivers analysis, market structure, etc.

- Forecast market growth leveraging (PGM - an internal tool) on an annual basis to inform PEP's long-term expectations for growth

- Collate and format consumer learnings from custom insight outputs, sales performance reporting, industry periodicals, and social listening resources, etc to help inform and develop future consumer insights strategies

- Provide responses to ad-hoc follow-ups when double-click (additional questions) required with tables/charts for relevant data

- Support relationships with the key end-user stakeholders in AMESA and region offices

Requirements:

- 6+ years of experience in the field of analytics

- Ability to convert insights into story that answers critical business questions.

- Hands on in coding in tools like R and Python

- Hands on in MS excel (advanced) and SQL

- Understanding of data structures, adept in data cleaning, structure, and aggregation as per need

- End-to-end project management.

- Expert of statistical techniques like regression, forecasting, decision trees and modelling process- concept and coding.

- Ability to design model architecture and translate business problem into analytical problem

- Ability to visualize data set and identify KPIs that will help decision making.

- Good PowerPoint skills.

- Experience in ML techniques, cloud compatible tech stacks, understanding of data pipeline creation preferable