Applied Data Scientist / Machine Learning Engineer (Decision Intelligence)

WorkWave is a remote-first company focused on developing innovative solutions that enhance customer decision-making. They are seeking an Applied Data Scientist or Machine Learning Engineer to build and scale ML-powered products, focusing on end-to-end machine learning ownership and product integration.

Responsibilities:

• Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products
• Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring
• Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools
• Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact
• 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
• Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time
• Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users
• Bring strong judgment around when to use traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic
• Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation
• Guide and mentor other data scientists, ML engineers, analysts, and cross-functional partners in applied ML best practices

Requirements:

• 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
• 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
• Strong understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling
• Experience with real-world, imperfect product datasets is essential
• 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)
• Hands-on experience operating within cloud environments (AWS, GCP, or Azure)
• 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