Applied Data Scientist / Machine Learning Engineer (Decision Intelligence)

Role Overview

WorkWave is seeking a product-minded Applied Data Scientist / Machine Learning Engineer (Decision Intelligence) to build, ship, and scale ML-powered products that improve how customers make decisions and operate their businesses. The position is not research-only and emphasizes end-to-end ownership, from problem definition through deployment, measurement, iteration, and long-term support.

What You’ll Do

• End-to-End ML Ownership: Develop ML capabilities such as forecasting, recommendation, ranking, optimization, and decision intelligence for customer-facing SaaS.
• Pipeline & Model Development: Build reliable data/feature pipelines and model lifecycle work including discovery, experimentation, validation, deployment, and monitoring.
• Product Integration: Partner with Product Managers and Software Engineers to embed ML into product workflows and decision-making experiences.
• Pragmatic Prototyping: Move prototypes to production while balancing accuracy, explainability, latency, maintainability, and business impact.
• Evaluation, Experimentation, and Strategy: Create offline/online evaluation plans (quality, drift, reliability), and design A/B tests and causal measurement frameworks.
• Data Health & Feedback Loops: Work with data teams on high-quality features and implement feedback loops to improve product performance over time.
• Platform & MLOps Support: Help manage and optimize cloud data infrastructure and proactively monitor data health.
• Strategic Technical Judgment: Choose between traditional ML, statistical models, LLMs, heuristics, or simpler logic, making trade-offs for customer impact.
• Roadmap Influence: Communicate what ML can/can’t solve and where ML creates real differentiation.
• Mentorship: Guide and mentor data/ML/analytics partners on applied ML best practices.

Who You Are

• Shipped ML to production products and can tackle ambiguous product problems with measurable outcomes.
• Product-first mindset: prioritizes user trust, explainability, latency, adoption, and measurable product impact—not only notebook accuracy.
• Execution across the stack: values pipeline quality, customer trust, and time-to-insight alongside model implementation.

What You’ll Bring

• 3+ years professional experience (ideally 5+) in applied data science, ML, or ML engineering, including shipping production models.
• Python and hands-on applied ML frameworks such as Scikit-Learn, XGBoost, PyTorch, or TensorFlow.
• Strong SQL expertise.
• (Additional ML/modeling requirements continue beyond the provided text.)