Data Abuse & Detection Engineer

Job highlights

• Required Qualifications Skills . 4+ years in fraud detection,abuse detection,anti-bot,or trust safety engineering. . Strong SQL and Python
• Demonstrated experience building detection logic,not only operating a vendor tool.
• Preferred . .

About the Role

Polus provides identity and contact data to enterprise clients through APIs and bulk delivery. This role detects and stops misuse of that data: credential sharing, reselling access, scraping, queries that fall outside a client s permitted purpose, and automated harvesting. You ll build the detection systems, not just monitor them. The work is behavioral analysis at scale: finding the few accounts whose usage patterns don t match their stated business, and turning those findings into enforced controls.

Key Responsibilities

• Build and maintain detection logic over high-volume API request logs and usage data in SQL and PySpark.
• Develop behavioral baselines per client and per account, and flag deviations such as query velocity, search-pattern shape, geographic spread, and credential reuse.
• Investigate flagged activity end to end and write clear findings for account management and compliance.
• Turn confirmed abuse patterns into automated rules (rate limits, blocks, and alerts), working with the API platform team.
• Measure detection quality through false positive rates, time to detect, and coverage, and tune accordingly.
• Maintain a library of known abuse typologies and the signals that identify each.

Required Qualifications Skills

• 4+ years in fraud detection, abuse detection, anti-bot, or trust safety engineering.
• Strong SQL and Python. Hands-on PySpark or equivalent distributed processing on large datasets.
• Demonstrated experience building detection logic, not only operating a vendor tool.
• Experience with anomaly detection approaches, whether statistical, rules-based, or ML-assisted, and judgment about when each fits.
• Ability to write investigation findings that non-technical stakeholders can act on.
• Comfort working under strict data-handling controls.

Preferred

• Experience with API abuse specifically: scraping, credential sharing, or account resale.
• Background in regulated data environments such as payments, lending, or consumer data.
• Familiarity with Hive, Elasticsearch and Datadog.
• Graph-based analysis for linking related accounts.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.