Come [legally] hack with us the data on the largest exchange that’s running our world. Not NASDAQ; the one with way more events - the Global Ads Exchanges, where millions of ads are born and clicked every second. Step behind the curtain of algorithms and competitors that move $1T of annual budgets. Plunge into a world of ISP-volumes of traffic, sub-second predictions, and TBs of live, real-world data. Use cutting-edge analysis, ML, engineering, or just plain hacker-like thinking to out-perform the market.

Arpeely is a Data-Science startup, leveraging data analysis, ML, engineering, and multi-disciplinary thinking to gain a market edge and exploit hidden opportunities in real-time advertising. Processing over 1,000,000 requests per second and serving over 20B sub-second predictions daily, we build and operate Machine Learning algorithms running on the world’s largest Real-time-bidding (RTB) Ad-Exchanges.

Join the Data Battle Against Ad Fraud

Dive into the fast-paced, high-stakes world of real-time advertising, where fraudsters are constantly inventing new ways to game the system—and we’re here to stop them. This isn’t about catching yesterday’s fraud. It’s about building systems that predict, detect, and outsmart malicious behavior as it happens, across billions of data points and signals.

At Arpeely, fraud isn’t just a problem—it’s a puzzle. We’re looking for a sharp, curious, and experienced Senior Fraud Data Analyst to take a leading role in uncovering abuse, analyzing behavioral anomalies, and developing data-driven strategies to stay one step ahead. If you love connecting the dots in messy data, challenging assumptions, and working with others to build smarter defenses—this role is for you.

What You’ll Do

• Analyze & Interpret Data: Use SQL and Python to query large datasets, detect anomalies, conduct root cause analysis, and uncover fraud patterns while also providing prescriptive insights to optimize business performance.
• Drive Business Insights: Identify trends in ad performance, diagnose inefficiencies, and provide data-driven recommendations that impact KPIs and company strategy.
• Combat Fraud in Real Time: Identify fraudulent trends, investigate suspicious traffic patterns, and take action to mitigate risk.
• Develop & Improve Detection Models: Work with data engineers and data scientists to refine fraud detection and optimize real-time prevention strategies.
• Collaborate Across Teams: Partner with business stakeholders to strengthen fraud defenses across Arpeely’s platforms.

Stay Ahead of Fraudsters: Keep up with evolving fraud tactics and propose proactive solutions to prevent emerging threats

Requirements

• SQL Expertise: 5+ years of experience working with complex queries on large datasets (BigQuery experience is a plus).
• Fraud Analysis & Data Interpretation: Proven ability to detect anomalies, investigate suspicious patterns, and mitigate fraud.
• Python Proficiency: Experience using Python for advanced analytics and automation.
• Critical Thinking & Problem-Solving: Strong analytical skills to diagnose fraud root causes and implement solutions in a fast-paced environment.
• Communication Skills: Ability to present insights, investigations, and recommendations clearly to both technical and non-technical stakeholders.
• Self-Starter Mentality: Independent, proactive, and motivated to tackle fraud at scale.
• Ad Fraud & Digital Advertising Knowledge is an advantage: Familiarity with IVT (Invalid Traffic), bot detection, and ad fraud prevention tools

Why Work at Arpeely?

• Make an Impact: Directly combat ad fraud and protect millions in advertising spend.
• Work with Cutting-Edge Tech: Leverage ML, automation, and real-time analytics to outsmart fraudsters.
• Autonomy & Growth: A fast-paced, high-impact environment where you’ll have ownership over key fraud-fighting initiatives.
• Collaborative Team: Work alongside experts in data, engineering, and AI to solve one of digital advertising’s biggest challenges.

If you’re ready to take on fraud at scale and make an immediate impact, join us at Arpeely!