Lead complex, multi-signal investigations (e.g., account takeover, automation, fraud rings, API abuse) and produce clear, actionable remediation plans.
Serve as the CX technical authority in escalations, guiding hypothesis formation, analysis strategy, and root-cause conclusions.
Translate customer context (risk tolerance, user base, operational constraints, compliance) into measurable detection and tuning objectives.
Identify emerging fraud patterns and cross-tenant trends; conduct quarterly intelligence briefs for customers and recommended mitigations.
Design and recommend detection strategies combining rules, features, and (where applicable) model-driven scoring to reduce fraud while controlling false-positive costs.
Develop reusable feature definitions and tuning approaches that can be applied across customers (not only one-off tenant fixes).
Partner with engineering and products to shape product roadmap priorities, improve explainability and investigation tooling (e.g., “why did this fire,” attribution, drill-down paths).
Define and own CX measurement systems: detection quality, false-positive impact, operational metrics, customer outcomes, and performance over time.
Lead offline evaluation and backtesting methodologies for fraud controls and customer configuration changes.
Drive experimentation practices (A/B where feasible, quasi-experimental, pre/post with controls) and ensure results are decision-grade.
Detect data drift and performance degradation signals; propose retraining triggers and/or mitigation plans in partnership with ML/Engineering.
Create “investigation packs” for R&D: evidence, root cause hypothesis, recommended technical changes, and measurable acceptance criteria.
Mentor and develop other DS/analysts on CX through playbooks, code reviews, investigation standards.
Partner with GTM teams to quantify customer value, communicate outcomes, and support renewals/expansions through data-backed narratives.

Requirements

6+ years in data science / applied analytics roles with increasing scope and ownership; fraud/risk/identity/security experience strongly preferred.
Expert-level SQL and strong Python for analytics and production-grade analysis (testing, modularity, version control habits).
Experience operating in customer-facing environments, including executive communication and handling escalations with calm, credible technical leadership with proven ability to lead end-to-end analytical initiatives.
Deep competence in statistics and evaluation:
hypothesis testing, segmentation, regression/classification metrics
time series / anomaly detection concepts
experimental design and causal inference fundamentals
Bachelor's degree in a quantitative field required; advanced degree preferred (MS/PhD).
Hands-on experience with real-time decisioning systems, streaming/event-based analytics, and latency-aware detection constraints.
Familiarity with modern ML methods used in fraud: supervised classifiers, graph/ring detection approaches, anomaly detection, and model monitoring.
Experience deploying or operationalizing models (MLOps exposure), including monitoring, drift detection, and retraining governance.
Experience with identity/authentication ecosystems (MFA modalities, risk-based authentication, device intelligence, bot defense signals).

Benefits

Health insurance
Flexible work hours
Paid time off
Professional development opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythonstatisticshypothesis testingsegmentationregression metricstime series detectionanomaly detectionexperimental designMLOps

Soft Skills

technical leadershipexecutive communicationcalm under pressurementoringanalytical initiative leadership

Certifications

Bachelor's degree in quantitative fieldadvanced degree (MS/PhD) preferred