FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in data science with a focus on customer-facing roles, particularly in AML and fraud detection. Proficient in translating complex model findings into actionable business insights while effectively communicating with both technical and non-technical stakeholders.
Highest-signal resume keywords
Data Science ExpertiseAML And Fraud DetectionPython And SQL ProficiencyCustomer-Facing Presentation SkillsTransaction Monitoring Knowledge
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data ScienceAMLFraud DetectionTransaction MonitoringPythonSQLMachine LearningModel ValidationBacktestingData Analysis
Soft Skills
Judgment Under AmbiguityOwnership MentalityCommunication SkillsProblem-SolvingCustomer Engagement
Tools & Technologies
Production ML ToolingExplainable AIHybrid Detection Approaches
Industry Keywords
Financial Crime AnalyticsInvestigator False-Positive CostsCustomer Value PartnersComplianceRisk Management
Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Tune and optimize detection models and thresholds against customers’ live transaction data
- Investigate missed cases, alert quality, and pattern drift
- Convert analytical findings into tuning or configuration changes
- Build customer-facing analyses showing investigator productivity gains, false-positive cost reduction, and detection improvements
- Present findings directly to customers and defend methodology to data science or compliance teams
- Answer customer questions about model decisions
- Partner with regional Customer Value Partners on account strategy, value realization, and renewal conversations
- Feed customer patterns and findings into Hawk’s model and product functions
- Distinguish customer-specific tuning needs from systemic product gaps
- Document tuning decisions for audit and regulatory review
- Validate model changes through backtesting, sample review, and sign-off before production release
Requirements
What you’ll need- 5–7 years as a data scientist in a customer-facing role
- Experience presenting analysis and defending model decisions directly to clients
- Real experience in AML, fraud detection, or financial crime analytics
- Understanding of transaction monitoring, typologies, and investigator false-positive costs
- Strong hands-on skills in Python, SQL, and production ML tooling
- Ability to make sound judgments under ambiguity and determine when model changes require human review
- Ability to act as the technical voice with customer risk, compliance, or data science stakeholders
- Track record of translating model performance into business value for non-technical stakeholders
- Comfort with ambiguity and live production systems
- Ownership mentality and ability to investigate drifting detection performance proactively
- Fluent English, including speaking, reading, and writing
- Fluent German, including speaking, reading, and writing
- Experience specifically in transaction monitoring or payments fraud detection at a bank, payment provider, or vendor serving them is a bonus
- Familiarity with explainable AI and rules-based hybrid detection approaches is a bonus
Benefits
Comp & perks- Hybrid work model with onsite work 2–3 days per week
- Opportunities to grow professionally
- Culture of mutual trust, support and passion
- AI Notetaker interview recording/transcription with voluntary consent; recording used internally for evaluation purposes only
- Recording consent can be withdrawn at any time without negative impact on the application