Machine Learning Engineer

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.

Responsibilities

• 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

Qualifications