Senior Data Analyst

Harness is the AI Software Delivery Platform company, led by technologist and entrepreneur Jyoti Bansal (founder of AppDynamics, acquired by Cisco for $3.7B). Harness has raised approximately $570M in funding and is valued at $5.5B, backed by leading investors including Goldman Sachs, Menlo Ventures, IVP, Unusual Ventures, Citi Ventures, and more. As AI accelerates code creation, the real bottleneck has shifted to everything after the code – testing, deployments, application security, reliability, compliance, and cost optimization. Harness brings AI and automation to this “outer loop,” helping teams ship software faster while maintaining security and governance throughout the entire software delivery lifecycle. Powered by Harness AI and the Software Delivery Knowledge Graph, the Harness Platform applies deep context and intelligent automation across the software delivery lifecycle with governance and policy-driven controls embedded throughout the platform. Over the past year, Harness powered over 185M deployments, 82M builds, 18T flag evaluations, 8M security scans, 9.1B optimized tests, 3T protected API calls, and helped manage $2.8B in cloud spend — enabling customers like United Airlines, Morningstar, and Choice Hotels to accelerate releases by up to 75%, reduce cloud costs by up to 60%, and achieve 10x DevOps efficiency. With a global team across 14 offices and 25 countries, Harness is shaping the future of AI software delivery — and we’re looking for exceptional talent to help us move even faster. Position Summary As we scale, we are hiring a Bot Abuse Analyst to own operationalization of Bot Abuse Protection customers and drive their success. This is a high-ownership, high-visibility role at the intersection of fraud analytics, customer engagement, and product enablement. The Bot Abuse Analyst will: Work with customers to uncover their pain points and abuse patterns Recommend and optimize risk algorithms balancing fraud prevention and customer experience Influence model improvements through structured analysis and real-world feedback Leverage AI to uncover novel bot abuse patterns on the customer’s traffic Build ML pipelines for custom detection logic to stop attacks Support automation of existing detection workflows Own POV(Proof Of Concept) outcomes Act as the analytical bridge between Customer Success, Data Science, and Engineering Serve as the feedback loop between customers and product, helping shape the product’s efficacy Proactively suggest generic novel algorithms that can detect abuse patterns across customers Own the Data Science conversation with customers during QBRs This role is ideal for someone with strong fraud domain knowledge (ATO, scraping, carding, bot abuse) who enjoys deep analytical work and customer-facing impact. Key Responsibilities You will play a key role in enabling the following workflow: CS → Onboarding Request → Traffic Analysis → Policy Recommendations → DS Review → Activation → Monitoring Over time, this process will increasingly be automated — and you will help design and operationalize that automation. Traffic Discovery Abuse Investigation Analyze customer traffic to identify abuse vectors and attack patterns Perform deep investigations into bot behavior and fraud signals Detect emerging abuse use cases across traffic streams Identify instrumentation or visibility gaps affecting detection quality Partner with Data Science to convert findings into structured problem statements Customer Onboarding Detection Enablement Analyze incoming traffic during customer onboarding Assess suitability for existing detections Identify data visibility gaps and recommend improvements Generate policy configuration recommendations (manual → assisted → automated over time) Validate detection efficacy and iterate based on performance feedback Custom Policy Strategy (POVs High-Touch Engagements) Diagnose customer-specific abuse cases during POVs Identify which variant of ... (truncated, view full listing at source)