Overview 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 Purpose. For more information on PepsiCo and the opportunities it holds, visit www.pepsico.com. Data Science Team works 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. 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. Responsibilities Active contributor to code development in projects and services Act as contributor in innovation activities Partner with data engineers to ensure data access for discovery and proper data is prepared for model consumption. Partner with ML engineers working on industrialization. Occasionally, coordinate work activities with Business teams, other IT services and as required. Communicate with business stakeholders in the process of service design, training and knowledge transfer. Support large-scale experimentation and build data-driven models. Set KPIs and metrics to evaluate analytics solution given a particular use case. Refine requirements into modelling problems. Influence product teams through data-based recommendations. Research in state-of-the-art methodologies. Create documentation for learnings and knowledge transfer. Create reusable packages or libraries. Qualifications 11+ years’ experience building solutions in the commercial or in the supply chain space. 10+ years working in a team to deliver production level analytic solutions. Fluent in git (version control). Understanding of Jenkins, Docker are a plus. 10+ years’ experience in ETL and/or data wrangling techniques. Fluent in SQL syntaxis. 10+ years’ experience in Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems. Experiences with Deep Learning are a plus. 9+ years’ experience in developing business problem related statistical/ML modeling with industry tools with primary focus on Python or Scala development. Business storytelling and communicating data insights in business consumable format. Fluent in one Visualization tool. Strong communications and organizational skills with the ability to deal with ambiguity while juggling multiple priorities Experience with Agile methodology for team work and analytics ‘product’ creation. Fluent in Jira, Confluence. Experience with Azure cloud services is a must. Experience in Reinforcement Learning is a plus. Experience in Simulation and Optimization problems in any space is a plus. Experience with Bayesian methods is a plus. Experience with Causal inference is a plus. Experience with NLP is a plus. Experience with working with FAIR data is a plus. Experience with Responsible AI is a plus. Experience with distributed machine learning is a plus