Applied Data Scientist / Machine Learning Engineer (Decision Intelligence)

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.

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.

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.

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 technical trade-offs clearly to product, engineering, and non-technical business stakeholders.

Experience with decision intelligence, forecasting, customer behavior modeling, workforce/route optimization, or operational intelligence products.

Experience with LLMs, GenAI, or agentic workflows applied to real product use cases.

Prior experience acting as a Senior or Lead scientist responsible for guiding technical direction.