Senior Data Scientist (Future Opportunity)

View full description

Requirements:

• 6+ years of hands-on experience building and deploying production quality predictive and/or prescriptive models.
• Strong data acquisition skills using SQL in modern technical stacks (PostGres, AWS, Snowflake).
• 6+ years of post-graduate experience working with open source data science toolkits including Python (numpy, pandas, scikit, PySpark) or R (Tidyverse/Tidymodels). Strong preference for candidates who can capably translate between Python and R.
• 2+ years experience in deploying models to production environments through APIs, or batch processes.
• 3+ years experience in at least TWO of the following DS methods: Supervised and Unsupervised ML (regression, classification, etc.), Econometric modeling (e.g., time-to-event/survival analysis), Reinforcement learning (e.g., multiarm bandits for price or intervention optimization), Operations research (e.g., combinatorial optimization, traveling salesman, linear programming).
• Demonstrated experience with reproducible data science environments including creation of virtual environments, library management, and version control using Git/Github.
• Demonstrated experience writing up results using literate programming workflows (Quarto preferred).

Responsibilities:

• Combine DS strategies, programming expertise, and theoretical understanding of techniques to deliver advanced ML solutions for problem solving across the enterprise.
• Scope, explore, specify, test, and deploy, and observe accurate and stable models into production environments.
• Utilize modern, open-source data scientific workflows (SQL, R/Python, Github) to create highly accurate or explainable predictive/prescriptive models.
• Own models in production and re-train when necessary.
• Demonstrate a product mindset to deliver portable components that can be deployed into existing products OR converted into stand-alone data products.
• Partner closely with BI, Engineering, Product Management, DevOps, and business stakeholders to create business-aligned, stable workflows.
• Communicate results and model specifications to appropriate stakeholders.
• Mentor, train, and assist aspiring data science professionals.