Data Abuse & Detection Engineer
- Employment Type - Full-Time
- Location - Trivandrum
- Experience - 4 – 8 Years
- Notice Period -Immediate Joiner
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.
Apply now
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