Senior Applied Data Scientist / ML Engineer

Analityka rynku

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Opis

What You'll Do - From Raw Data to Better Decisions Own the Outcome: Take an ambiguous customer problem, decide whether ML is even the right answer, build it, and stay with it until customers are acting on it. - Learn the Domain: Get fluent in the semantics of how our customers operate—what a route, a crew, or a service history actually means. A model that is accurate but wrong about the domain creates nothing. - Build the Data You Need: When the features don't exist, create them in Snowflake and dbt rather than waiting for someone else to. - Prove It and Make It Felt Make the Value Legible: Decide how a prediction reaches the customer so they understand it, trust it, and act on it—then report realised impact back to Product and the business in numbers that hold up. - Measure Honestly: Define offline and online evaluation for model quality, drift, and reliability, and design the A/B tests or causal analyses that prove a feature improved customer outcomes. - Deliver Ship and Operate: Deployment, testing, versioning, monitoring, and drift detection. Delivery is part of the job, not a handoff. - Embed with Product: Partner with Product Managers and Software Engineers to put ML inside real product workflows—and say clearly when ML isn't the answer. Who We Are - The Owner: You measure your work by whether customers made better decisions, not by whether the model shipped. - Closer to the Data and the Customer: You'd rather spend a week understanding what the data means than a week t

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