Risk Analyst II, Account Integrity

Amazon's Account Integrity Team (AIT) within Buyer Risk Prevention (BRP) protects hundreds of millions of customers by identifying and stopping fraudulent activity before it causes harm. The Risk Mining function is where human ingenuity meets machine learning - our team detects emerging modus operandi (MOs) that automated systems alone can't catch, and feeds those signals back into scalable upstream defenses. Nowhere is that fight sharper than in Anti-Automation, where we go head-to-head with bots, scripted attacks, and automated abuse that evolves by the hour. If you want to do investigative work that directly translates into customer trust at global scale, this is the role.

The Risk Analyst II (Risk Mining) is a senior individual-contributor role that sits at the intersection of three disciplines - analyst, strategist, and ML partner. You will join the Anti-Automation Risk Mining team in India - a team of analysts, investigators, and builders who hunt down automated abuse before it scales. As the analyst, you run deep dives on bot and scripted-attack behavior and turn raw signal into high-quality MO documentation. As the strategist, you shape where the vertical invests its detection effort and anticipate how the adversary will evolve. As the ML partner, you work hand-in-hand with Machine Learning and Engineering to convert your findings into scalable, automated upstream defenses and close the feedback loop into models and rules.

This is a hands-on, high-ownership role with broad cross-team influence, reporting to Risk Mining leadership. You are accountable for the quality, speed, and impact of your investigations - detecting automated MOs, driving measurable gains in detection speed and precision, mitigating risk, and clearing the blockers that stand between the team and impact. As a senior IC, you are expected to raise the bar for investigation quality across the vertical and influence detection strategy well beyond your own caseload.

Key job responsibilities

- Lead complex investigations into emerging automated abuse - running deep dives on bots, scripted attacks, and automated MOs before they scale, and setting the quality bar others follow.

- Own detection and mitigation strategy for the Anti-Automation vertical - prioritizing the highest-risk automated MOs and anticipating how bots and scripted attacks will evolve.

- Partner deeply with Machine Learning and Engineering teams to turn your findings into scalable, automated upstream defenses, and own the feedback loop back into models and rules.

- Translate investigative signal into ML features, detection logic, and rule improvements - acting as the bridge between front-line investigation and automated defense.

- Produce high-quality deep dives, MO write-ups, and analyses, and own vertical-level metrics for detection volume, speed, and precision.

- Collaborate with the other Risk Mining verticals and partner teams (Payment Risk, Abuse Risk Mining) on joint investigations and shared signals.

- Serve as a subject-matter expert on high-severity incidents and escalations.

- Champion automation and GenAI opportunities to amplify throughput and quality, and measure the realized ROI from those investments.

A day in the life

No two days look the same when the automated-abuse landscape shifts in real time - your week balances hands-on investigation, strategy, and close ML partnership to keep Anti-Automation ahead of emerging threats:

Analyst (Investigation & Analysis)

- Running deep dives on the highest-risk automated MOs and following the signal in the noise and take mitigation action.

- Pulling and analyzing large datasets to size impact, confirm hypotheses, and sharpen detection.

- Documenting findings and MO write-ups so they can be turned into durable, scalable defenses.

Strategy (Building What's Next)

- Shaping the detection and mitigation strategy for your slice of Risk Mining - anticipating how automated abuse will evolve and positioning the team to stay ahead.

- Championing GenAI and automation opportunities as a core part of the vertical's strategy.

ML Partnership (Scaling the Defense)

- Partnering with ML and Engineering to turn your findings into automated defenses that scale.

- Translating investigative signal into features, detection logic, and rule improvements.

- Owning the feedback loop so model and rule performance keeps improving on the MOs you surface.

Tactical (Keeping the Engine Running)

- Reporting detection metrics and progress on key goals.

- Responding to live escalations when a new automated attack surfaces.

About the team

The Risk Mining team within Account Integrity spans India, US, and Costa Rica, and works for Amazon's worldwide marketplace to detect and disrupt bad-actor behavior.

Our mission is simple: find the signal in the noise before bad actors cause harm. We operate across three risk verticals - Anti-Automation, Account Takeover, and Bad Actor Account - and partner closely with Machine Learning, Engineering, and Product teams to turn our investigative findings into scalable upstream defenses. The Anti-Automation vertical sits at the front line of that fight, taking on bots and scripted abuse at the scale.

If you want to do work where your findings directly translate into customer trust at global scale - and where the adversary never stops evolving - this is the team.

Basic qualifications

- Bachelor's degree or equivalent

- Experience with advanced use of SQL for data mining and business intelligence, or experience with data analytics platforms (Power BI, Python, SQL, Tableau)

- Experience delivering results for large, cross-functional initiatives/projects

- 5+ years of experience in an analytics or data-driven role (e.g. business analyst, risk analyst, data analyst, operations analyst, or research analyst) or similar quantitative/analytical roles across any domain

Preferred qualifications

- 3+ years of program requirements definition and data and metrics leveraging to drive improvements experience

- Experience handling confidential information

- Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership

- Master's degree in Math, Statistics, Computer Science, or related Science field, or experience in data

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