Sr Applied Data Scientist/Engineer, Decision Intelligence
Quick Summary
You've quantified the business impact of a model you shipped—adoption, outcome, dollars—and presented it to people who were never going to read your notebook.
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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.
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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.
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Build the Data You Need: When the features don't exist, create them in Snowflake and dbt rather than waiting for someone else to.
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Make the Value Legible: Decide how a prediction reaches the customer so they understand it, trust it, and act on it—then report realized impact back to Product and the business in numbers that hold up.
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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.
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Ship and Operate: Deployment, testing, versioning, monitoring, and drift detection. Delivery is part of the job, not a handoff.
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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.
We build with coding agents. You set direction and targets, review output critically, and build the harnesses—scaffolding, context, tests, review loops—that make the next model faster to ship. The leverage is in the verification: the backtests, eval scaffolding, and data checks that make generated work safe to trust.
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Working With Agents: You've used coding agents on real modeling or engineering work, you can tell correct output from merely plausible output, and you invest in the scaffolding that makes the next model faster to ship.
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Decision Intelligence: Experience with decision intelligence, forecasting, customer behavior modeling, workforce/route optimization, or operational intelligence products.
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Prior experience as a senior or lead scientist or engineer responsible for guiding technical direction.
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LLM or agentic workflows shipped into a product
Location & Eligibility
Listing Details
- Posted
- August 24, 2026
- First seen
- August 24, 2026
- Last seen
- August 25, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 87%
- Scored at
- August 24, 2026
Signal breakdown

WorkWave provides cloud-based software and fintech solutions for field service businesses, helping them manage operations, sales, and customer interactions. The company serves industries like pest control, lawn care, and cleaning.
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