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HAWK

Fraud detection and AML platform

Customer Data Scientist

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
Full-timeRemote in USA$150k - $225k /yrRemote

About the Job

Requirements

  • 5-7 years of experience as a data scientist in a customer-facing role, presenting analysis and defending model decisions directly to clients.
  • Experience in AML, fraud detection, or financial crime analytics, including understanding transaction monitoring, typologies, and the investigator costs of false positives.
  • Hands-on skills in Python, SQL, and machine-learning tooling used in production.
  • Comfort serving as the technical voice with customer risk, compliance, or data science stakeholders and responding to questions.
  • A track record of translating model performance into actionable business value for non-technical stakeholders, including investigator hours saved, false-positive cost avoided, and cases caught.
  • Comfort working with ambiguity and live production systems rather than only offline research problems.
  • An ownership mentality, including proactively investigating changes in an account's detection performance.

Responsibilities

  • Tune and optimize detection models and thresholds against each customer's live transaction data, balancing detection effectiveness against false-positive load.
  • Investigate missed cases, alert quality, and pattern drift, and turn findings into tuning or configuration changes.
  • Build analyses that communicate investigator productivity gains, false-positive cost reduction, and detection effectiveness improvements to customer compliance and risk leadership.
  • Present findings directly to customers, defend methodology to their data science or compliance teams, and answer questions about model decisions.
  • Partner with regional Customer Value Partners on account strategy and identify model changes that can advance value realization or renewal conversations.
  • Feed patterns and findings back into model and product functions, distinguishing customer-specific tuning from systemic gaps.
  • Document reasoning behind tuning decisions to support customer audits or regulator reviews.
  • Validate model changes before deployment through backtesting, sample review, and sign-off discipline.

Desired Qualifications

  • Experience in transaction monitoring or payments fraud detection at a bank, payment provider, or vendor serving them.
  • Familiarity with explainable AI and rules-based hybrid detection approaches.

About the Company

HAWK

Fraud detection and AML platform

Hawk.ai provides AI-powered fraud detection and AML solutions for banks, fintechs, e-commerce, and insurers. Its platform analyzes transactions and user behavior in real time to spot suspicious patterns, using machine learning models that continuously learn and improve to adapt to new fraud tactics. It offers customizable risk scoring, transaction monitoring, and case management, with automated reporting, audit trails, and compliance monitoring to help meet AML regulations. The product integrates easily with existing systems and scales from small businesses to large enterprises, enabling rapid action to prevent financial crime. Hawk.ai differentiates itself by its focus on adaptive ML models, real-time analytics, and built-in regulatory compliance features tailored to multiple industries. The company’s goal is to protect assets, maintain customer trust, and uphold the integrity of financial transactions by staying ahead of fraud and money laundering threats.

Company size
201-500
Company stage
Series C
Total funding
$83M
Headquarters
Munich, Germany
Founded
2018

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