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 engineers, analysts, and cross-functional partners in applied ML best practices.

WHO YOU ARE:

The Proven Builder: You have shipped ML into real products. You are comfortable starting with an ambiguous product problem, figuring out if ML is the right solution, building it, and measuring whether it worked.

Product-First Architect: You care about product impact as much as model performance. You know that a model with slightly lower accuracy but higher trust, faster inference, better explainability, and stronger user adoption is the better product decision.

A Multi-Disciplinary Executioner: You understand that a model is only as good as the pipeline feeding it. You prioritize usability, "Time to Insight," and customer trust as much as you do code efficiency.

WHAT YOU’LL BRING:

Experience: 3+ years (ideally 5+) of professional experience in applied data science, machine learning, or ML engineering, including hands-on experience building and shipping models into production products. Experience with SaaS products is highly valued.
Technical Core: Strong Python skills and hands-on experience with applied ML libraries and frameworks (e.g., Scikit-Learn, XGBoost, PyTorch, TensorFlow). Solid SQL expertise is required.
ML & Modeling Depth: Strong understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling. Experience with real-world, imperfect product datasets is essential.
Ops & Orchestration: Familiarity with MLOps concepts (model versioning, feature pipelines, orchestration via Airflow/dbt/Dagster, monitoring, drift detection) and modern data platforms (e.g., Snowflake, BigQuery, Redshift, Databricks).
Cloud Infrastructure: Hands-on experience operating within cloud environments (AWS, GCP, or Azure).
Communication & Collaboration: Excellent communication skills with the ability to explain complex t

Applied Data Scientist / Machine Learning Engineer (Decision Intelligence) at WorkWave