Data Scientist

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 PepsiCos 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 Analyst

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 Azure 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 Pipelines leveraging the NGAA platform (Azure)
• Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
• Automate ML models deployments

• 4 -7 years of overall experience that includes at least 4+ years of hands-on work experience data science / Machine learning
• Minimum 4+ year of SQL experience
• Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers (Azure preferred) is preferred
• 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, R and database query languages likeSQL
• Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators
• Cloud (Azure) Experience in Databricks and ADF is desirable
• Familiarity with Spark, Hive, Pigis an added advantage
• Model deployment experience will be a plus
• Experience with version control systems like GitHub and CI/CD tools
• Experience is 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