Applied Data Scientist / Machine Learning Engineer (Decision Intelligence)

We are looking for a product-minded Applied Data Scientist or Machine Learning Engineer to help build, ship, and scale ML-powered products that directly improve how our customers make decisions, operate their businesses, and serve their own users.

This is not a research-only role, nor is it a service-oriented internal analytics position. We want someone who has taken machine learning from problem definition through experimentation, production deployment, measurement, iteration, and long-term ownership. You understand that great models are not just accurate in notebooks—they are usable, explainable, measurable, scalable, and valuable inside a real product.

Whether your background leans heavily toward Data Engineering/ML Ops or Applied Data Science, you have a strong bias toward shipping and an interest in bridging both worlds to bring AI to life.

WHAT YOU'LL DO:

Engineering & AI Enablement

End-to-End ML Ownership: Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products.

Pipeline & Model Development: Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring.

Product Integration: Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools.

Pragmatic Prototyping: Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact.

Ecosystem Ownership & Strategy

Evaluation & Experimentation: Define offline and online evaluation strategies, including model quality, drift, and reliability. Design A/B tests and causal measurement frameworks to prove ML features improve customer outcomes.

Data Health & Feedback Loops: Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time.

Platform & MLOps Support: Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users.

Product & Technical Direction

Strategic Judgment: Bring strong judgment around when to use traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic. Make practical trade-offs across model complexity and customer impact.

Roadmap Influence: Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation.

Mentorship: Guide and mentor other data scientists, ML enginee