Lead Data Scientist

Overview

The Sales Sr Analyst (Lead Data Scientist) is part of the Demand Accelerator group who works with FLNA & PBNA Sectors team. Provide Data science / data engineer support for the Demand Accelerator group who works with FLNA & PBNA Sectors, a part of the broader India Capability Center in Hyderabad, India.

This role will help to enable accelerated growth for PepsiCo by building pipelines, aligning data, and performing predictive and prescriptive analytics approaches for PepsiCo to drive actionable insights at the national, regional, store, and shopper level.

Key responsibilities will be leading the PFNA & PBNA Store-Potential for Shopper Insights North America, build and manage pipelines in a cloud environment, manage data and prepare data to be used for advanced analytics, artificial intelligence, machine learning, and deep learning projects.

Responsibilities

Lead the PFNA & PBNA Store-Potential for Shopper Insights North America.

Data Analysis and Modeling:Analyzing complex datasets to extract valuable insights and identify trends/patterns. Develop and implement predictive and prescriptive models to support business decision-making. Ensure data quality and integrity throughout the analysis process.

Lead, Mentor and Train, Team members/New Hires.

Data Analysis and Modeling:Analyzing complex datasets to extract valuable insights and identify trends/patterns. Develop and implement predictive and prescriptive models to support business decision-making. Ensure data quality and integrity throughout the analysis process.

Working with huge and varied datasets to find sources of strategic advantage in go-to-market and execution, with a focus on Organized Trade, Traditional Trade and Away-From-Home

Lead Support maintenance for PBNA (39 Markets) & PFNA (89 Zones)

Build analysis in Python with accompanying documentation. ETL Pipelines Support & Maintenance.

Lead role in steering Data Processing & Automation initiatives.

Azure Infrastructure Optimization. SalesPro+ Pipeline support & maintenance.

Develop algorithms and building predictive & prescriptive models needed to automate execution and the generation of insights, to enable efficiencies that allow colleagues to focus on value added tasks.

Support Data Science led to various requests from BU stakeholders

Lead Store-Potential Optimization (C&G Modeling, LF Accuracy Enhancement, Brand Inclusion, Existing Model optimization etc.)

Qualifications

Experience Required

Bachelor's or advanced degree in a quantitative field for instance a Masters degree or PhD in data science or math (e.g., Computer Science, Mathematics, Statistics, Data Science) or equivalent experience

8 years + of relevant advanced analytics experience in Marketing or Commercial in either Retail, or CPG industries. Other B2C domains can be considered

Proven experience with medium to large size data science projects

Advanced knowledge of key data science techniques:

Data preparation and feature engineering

Supervised / Unsupervised learning (ML Based)

Regression Based techniques

Collaborative Filtering

Location Analytics & Intelligence

Proficiency in programming languages such as Python, R, or SQL.

Expertise in Statistical concepts and techniques (Descriptive, Predictive and Prescriptive

Experience with data processing frameworks (e.g., Hadoop, Spark, Snowflake)

Understanding of data engineering principles, data integration, and ETL processes.

Excellent problem-solving skills and the ability to translate business needs into data-driven solutions.

Strong communication and interpersonal skills, with the ability to effectively convey complex concepts to both technical and non-technical stakeholders.

Differentiating compentencies required

Think of competencies rather than qualifications, such as managerial or leadership skills, flexibility, dealing with ambiguity, working in a matrix organization, etc.

Strong understanding of CPG Advanced Analytics requirement and understanding of the Advanced Analytics agencies

Strong experience in designing end-to-end data science solutions, from problem identification to implementation and deployment

Familiarity with cloud platforms (such as AWS, Azure, or Google Cloud) and their data services is a plus.