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Customer Data Scientist

Hawk· Customer
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Remote$150k - $225kFull-timePosted 1mo ago

About the role

Customer-facing data science role focused on tuning AML/fraud detection models for live customer data, translating technical findings into business value, and ensuring regulatory defensibility.

Responsibilities

  • Tune and optimize detection models and thresholds against each customer's live transaction data, balancing detection effectiveness against false positive load, not just against a benchmark dataset.
  • Investigate detection performance deeply: dig into missed cases, alert quality, and pattern drift, and turn what you find into concrete tuning or configuration changes.
  • Translate technical findings into customer-facing insight: build the analysis that shows investigator productivity gains, false positive cost reduction, and detection effectiveness improvements in language a customer's compliance and risk leadership actually uses.
  • Sit in the room with customers directly. Present findings, defend your methodology to a customer's own data science or compliance team, and answer the hard “why did the model do this” questions live.
  • Partner closely with your regional Customer Value Partners on account strategy, informing where the model needs to change to unlock the next stage of value realization or a renewal conversation.
  • Feed patterns and findings back into Hawk's broader model and product functions, distinguishing between “this customer needs local tuning” and “this is a systemic gap worth fixing centrally.”
  • Own the regulatory defensibility of the tuning decisions you make. Document your reasoning so a customer's audit or regulator review holds up.
  • Bring rigor to how you validate model changes before they go live: backtesting, sample review, and sign-off discipline that protects the customer's compliance posture.

Qualifications

  • 5-7 years as a data scientist in a customer-facing role, presenting analysis and defending model decisions directly to clients.
  • Real experience in AML, fraud detection, or financial crime analytics is required.
  • Strong hands-on skills in the standard data science stack (Python, SQL, and production ML tooling).
  • Comfortable being the technical voice in conversations with risk, compliance, or data science stakeholders.
  • Track record of translating model performance into business value for non-technical stakeholders (investigator hours saved, false positive costs avoided, cases caught).
  • Genuine comfort with ambiguity and live production systems.
  • Ownership mentality; proactive in identifying and addressing drifting detection performance.

Skills

Required:Data science stackAML/fraud detection knowledge

Benefits

  • -
Last updated Oct 22, 2026. Interested? Apply on the company site.

About Hawk

Increase your risk coverage. Improve efficiency.

Award-winning AML & CFT technology powered by explainable AI increases your risk coverage, helps you identify more crime, and reduces your false positives.

AMLCFTfinancial crimeexplainable AItransaction monitoringpayment screeningcustomer screeningrisk rating
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