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We believe that acting ethically and responsibly is not only the right thing to do, but also the right thing to do for our business. At PepsiCo, we aim to deliver top-tier financial performance over the long term by integrating sustainability into our business strategy, leaving a positive imprint on society and the environment. We call this
Winning with Pep+ Positive . For more information on PepsiCo and the opportunities it holds, visit
Data Analytics & AI Overview:
With data deeply embedded in our DNA, PepsiCo Data, Analytics and AI (DA&AI) transforms data into consumer delight. We build and organize business-ready data that allows PepsiCo's leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise.The Data Science Pillar in DA&AI will be the organization where Data Scientist and ML Engineers report to in the broader D+A Organization. Also DS will lead, facilitate and collaborate on the larger DS community in PepsiCo. DS will provide the talent for the development and support of DS component and its life cycle within DA&AIProducts. And will support "pre-engagement" activities as requested and validated by the prioritization framework of DA&AI.
Data Science Senior Analyst: Hyderabad and Gurugram
Your role will be to be part of growing team based in Hyderabad, to create and support global digital developments for PepsiCo. These developments will be around topics like revenue management, supply chain, manufacturing, logistics. This Role focuses on Demand Forecasting in the Demand Planning functional area.You will be part of a collaborative interdisciplinary team around data, where you will be responsible of our continuous delivery of statistical/ML models. You will work closely with process owners, product owners and final business users. This will provide you the correct visibility and understanding of criticality of your developments.You will be an internal ambassador of the team's culture around data and analytics. You will provide stewardship to colleagues in the areas that you are an specialist or you are specializing.
ResponsibilitiesActive contributor to code development in projects and servicesAct as contributor in innovation activitiesPartner with data scientist working on discovery, prototypes and pilot. Focus on experiment tracking.Partner with data engineers regarding data pipelines and data versioning.Build, deploy or publish machine learning models that run efficiently in cloud pipelines.Heavy focus on data wrangling implementation, business rules implementation, feature engineering at scaleHelp drive optimization, testing, and tooling to improve quality (unit tests).Monitor model performance (model decay), working with data scientists assessing bias and fairness.Prototype new approaches and build solutions at scale.Support large-scale experimentation done by data scientists.Research in state-of-the-art methodologies.Create documentation for learnings and knowledge transfer.Create and audit reusable packages or libraries.
Qualifications8+ years working in a team to deliver production level analytic solutions.4+ years' experience in ETL (ADF and Data bricks) and/or data wrangling4+ years' experience in Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems. Experiences with Deep Learning are a plus.4+ years' experience in developing business problem related statistical/ML modeling with industry tools with primary focus on Python or Pyspark development.Strong communications and organizational skills with the ability to deal with ambiguity while juggling multiple prioritiesExperience with Agile methodology for team work and analytics 'product' creation. Fluent in Jira, Confluence.Experience in cloud-based development and deployment (Azure preferred)Fluent with Azure cloud services. Certification is a plus.Fluent in git (version control)Fluent in Docker
