Data Scientist

We are PepsiCo

PepsiCo is one of the world's leading food and beverage companies with more than $79 Billion in Net Revenue and a global portfolio of diverse and beloved brands. We have a complementary food and beverage portfolio that includes 22 brands that each generate more than $1 Billion in annual retail sales. PepsiCo's products are sold in more than 200 countries and territories around the world. PepsiCo's strength is its people. We are over 250,000 game changers, mountain movers and history makers, located around the world, and united by a shared set of values and goals.

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

PepsiCo 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&AI Products. And will support "pre-engagement" activities as requested and validated by the prioritization framework of DA&AI.

Data Scientist-Gurugram and Hyderabad

The role will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Machine Learning Services and Pipelines.

• Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope
• Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities
• Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards
• Use big data technologies to help process data and build scaled data pipelines (batch to real time)
• Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure/AWS/GCP Pipelines.
• Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
• Automate ML models deployments

• Minimum 3years of hands-on work experience in data science / Machine learning
• Minimum 3year of SQL experience
• Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers.
• BE/BS in Computer Science, Math, Physics, or other technical fields.
• Data Science – Hands on experience and strong knowledge of building machine learning models – supervised and unsupervised models
• Programming Skills – Hands-on experience in statistical programming languages like Python and database query languages like SQL
• Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators
• Any Cloud – Experience in Databricks and ADF is desirable
• Familiarity with Spark, Hive, Pig is an added advantage
• Model deployment experience will be a plus
• Experience with version control systems like GitHub and CI/CD tools
• Experience in Exploratory data Analysis
• Knowledge of ML Ops / DevOps and deploying ML models is required
• Experience using MLFlow, Kubeflow etc. will be preferred
• Experience executing and contributing to ML OPS automation infrastructure is good to have
• Exceptional analytical and problem-solving skills