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Applied data scientist or ML engineer to build models on raw operational data for customer decisions, such as routes, crews, and service history. Work includes creating features in Snowflake and dbt, deployment, monitoring, and drift detection on AWS. Stack is Python, scikit-learn, XGBoost or LightGBM, and SQL.
We are looking for a product-minded applied data scientist or engineer to turn raw operational data into products that measurably improve how our customers make decisions and run their businesses. Which label you carry matters less to us than whether customers end up better off.
This is not a research-only role, nor a service-oriented internal analytics position—and it is not a role for a model builder alone. We want an owner: someone who frames the problem, learns the domain, builds the data when it doesn't exist, ships the model, and stays with it until customers are acting on it and can measure the reward. The road runs through data engineering; deployment and testing are part of delivery, not a handoff. You understand that great models are not just accurate in notebooks—they are usable, explainable, and measurable inside a real product.
Whether you came to this work through statistics, software, analytics, or data engineering, you have a strong bias toward shipping.
WHAT YOU SHOULD KNOW ABOUT US
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EQUAL OPPORTUNITY
WorkWave is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees in accordance with the Equality Act 2010.
Don't meet every single requirement? Studies show that women and underrepresented groups are less likely to apply for jobs unless they meet every qualification. If you feel you could make a great impact in this role, we encourage you to apply anyway!
