Fraud Detection Specialist / Fraud SME

General

• Seniority: Senior profile preferred (5+ years of hands-on fraud or risk analytics). Strong candidates with 2 to 3 years who have built and owned detection logic in production are also welcome.

• Employment Type: Employee or Contractor.

• Schedule: Full-time.

• Work Arrangement: Full remote possible, with availability for periodic meetings, workshops, and collaboration sessions.

Responsibilities

Fraud Analysis & Pattern Discovery

• Work hands-on with event, transaction, behavioral, and device data to identify attack patterns, emerging scam typologies, and gaps in current detection.

• Investigate false positives and missed fraud, and trace them to their root cause in rules, signals, or model behavior.

• Track how online fraud evolves, including the attacker tooling behind it, and translate what changes into what we need to detect.

Detection Design

• Design and specify detection logic: rules for the rule engine, thresholds, risk indicators, and combinations of signals.

• Prototype detection ideas yourself (a query, a notebook, a scoring heuristic, a draft algorithm) and validate them against historical data before they become a requirement.

• Propose new features, thresholds, and score calibration for our models where the data supports it.

• Own the measurement of detection quality: run back-tests and post-release analysis, and hold us honest about whether what we shipped actually works in production.

Shaping the Product

• (Senior scope) Set the agenda for what we detect next: bring the evidence and the reasoning that decide which fraud problems get solved and in what order.

• Partner closely with research and data science: bring them the fraud problem, the labeled cases, and the domain reasoning; challenge and sanity-check model behavior from a fraud perspective.

• Work with engineering to get detection capabilities implemented correctly and tuned properly.

Working With Customers

• Run fraud scenario discussions and workshops with customers' fraud teams: what they are seeing, where they are losing money, what their current detection misses.

• Support post-incident analysis when new attack patterns appear, and feed those findings back into the product.

• (Senior scope) Act as the credible fraud expert in the room, with customers, prospects, and partners alike.

Some responsibilities are marked as senior scope. If you are earlier in your career, this is what you grow into here, not what we expect on day one.

Qualifications

Must Have

• SQL, plus Python or an equivalent analytical toolset, and the ability to work with data independently: exploring it, testing hypotheses, and prototyping detection logic.

• Experience owning the performance of a rule or a score in production: you built it, you watched how it behaved, and you tuned it.

• Solid understanding of fraud schemes and techniques in digital banking and payments.

• A structured, hypothesis-driven approach to analysis with strong statistical intuition: forming a hypothesis, testing it against data, and telling a genuinely predictive signal from an artifact of the data (base rates, sampling bias, noise in sparse or imbalanced labels, correlation vs. causation).

• Ability to explain complex detection concepts clearly to fraud analysts, engineers, researchers, and executives alike.

Additionally for Senior Profiles

• A track record of setting detection strategy, not just executing it: deciding what to detect, in what order, and defending that direction with evidence.

• Experience working with machine learning models in a fraud context: feature engineering, evaluating model output, tuning scores and thresholds.

Strongly Preferred

• Experience inside a bank's or PSP's anti-fraud team as a fraud analyst, data analyst, or fraud strategy specialist, building rules and anti-fraud strategies and living with their results.

• Comparable experience at a fraud prevention vendor, or in AML, credit risk, abuse and trust & safety, or security analytics where the adversary adapts to what you build.

In either case, ideally somewhere detection quality was measured rigorously and weak rules or signals had real consequences: operational cost, customer friction, or missed fraud.

Nice to Have

• Experience with behavioral biometrics, device fingerprinting, or other behavioral signals.

• Cyber security background relevant to fraud: understanding how account takeover, phishing and social engineering, malware and remote access tools, bots, and session hijacking work technically, and how they show up in device, network, and session data.

• Familiarity with rule engines, real-time decisioning platforms, or case management systems.

• Experience presenting to and advising external customers.

• Exposure to the product lifecycle and to working in a startup environment.

What We Value

• Extreme Ownership: Taking initiative, driving results, and passionately owning project outcomes.

• Deep Understanding: Preferring to understand topics and systems deeply and from first principles, over opinions, guesses, and the superficial.

• Integrity: Consistently adhering to strong moral and ethical principles; acting with honesty, accountability, and transparency.

• Collaboration: Building positive, efficient relationships and communicating precisely.

• Innovation and Adaptability: Continuously seeking improvement, being open to new ideas, and making data-driven decisions.