Collaborates cross-functionally and contributes expertise through actionable business intelligence to assist in complex organizational decisions; collaborates with Data Scientists - Quantitative Analysts to build machine learning models; supports tuning and deployment of models to production as RESTful APIs and/or batch jobs; streamlines the training and validation of models; adheres to engineering best practices and proactively identifies and resolves technical debt; builds and adopts new tools to accelerate the ML deployment lifecycle; engages with business partners to develop understanding of supply chain industry; collaborates with data scientists and software engineers on data design and reporting strategy across business functions to ensure cohesive alignment; directs appropriate architecture, modeling, harmonization, and governance of data; ensures strict data hygiene to ensure data is clean, accurate, concise, comprehensive, and complete; reviews work across team, and provides feedback for professional development; concisely explains complex analysis and insights to stakeholders; leverages research and analytics to support data-driven recommendations; builds and validates predictive models on structured/unstructured data; uses Python, R, SQL Server, MongoDB, and Snowflake; employs AWS architecture, Docker, Kubernetes, Spark, Kafka, Jenkins, Git (Gitlab), FastAPI, Kubeflow.

Must have a Bachelor's degree in Computer Science or Engineering. Must have six (6) months of experience in the job offered or as a Research Assistant.

Six (6) months of experience (inclusive of experience heretofore) in the following:

1) Python and SQL Server;
2) Data Cleaning and Data analysis; and
3) Designing dashboard.

Remote Work is permitted.

To apply, send cover letter and 2 resumes to the address provided and include reference number 9075.135.