Description

• Communicate complex technical findings to non-technical audiences in a way that influences decisions at scale.
• Collaborate closely with data engineering to influence data architecture, pipeline design, data modeling, and model-serving platforms.
• Build reusable ML components and services that can be consumed across multiple business units— aligning with a DaaS mindset.
• Drive continuous improvement across modeling techniques, LLM applications, and analytics tooling.
• Manage and develop a team of data scientists providing mentorship, coaching, and structured growth pathways.
• Defines clear product boundaries and backlogs , user stories, scope, acceptance criteria, and delivers product increments end- to end .
• Translate business pri orities into product roadmaps; what problems we solve, in what order.
• Lead by example through consistent technical contribution, reinforcing standards for model design, code quality, reproducibility, and documentation.
• Partner with the S enior Director, Data Product on resource planning, prioritization, and roadmap execution.
• Ensure data science artifacts are built with long-term maintainability, transparency, and reusability in mind.
• Own the full ML lifecycle from exploration to production: data extraction, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
• Lead the design, development, and maintenance of ML-driven products and services , including predictive models, risk scoring engines, exception detection services, and upcoming generative AI applications.
• Orchestrate cross-functional delivery across BI, data engineering, data science, and AI . .
• Establish and champion best practices across MLOps , model governance, CI/CD, testing, and monitoring.

Requirements

• Strong critical thinking, problem framing, and ability to structure ambiguous problems.
• 5–7+ years of hands-on experience in data science, applied machine learning, or AI, with proven end-to-end ownership of productionized ML systems.
• Skilled in SQL and either Python or R (experience with scikit-learn, tidymodels , MLlib or comparable frameworks ).
• Experience acting as a technical lead, senior IC, or informal mentor; ready to step confidently into a formal management role.
• Familiarity with the Posit ecosystem (Workbench, Connect, Quarto, vetiver) is a plus.
• Experience with distributed compute environments (Spark, Databricks) is a plus.
• Bachelor's or Master’s degree in Data Science , Computer Science, Statistics, or related field preferred.
• Comfortable balancing individual contribution with team leadership - able to switch between strategy, execution, coaching, and delivery.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance