Manager I, Machine Learning Data Ops

Amazon’s interview process typically consists of an initial phone screen followed by two to three virtual or onsite rounds focusing on leadership principles, data‑driven problem solving, and scenario‑based management questions. The difficulty is moderate to high, with emphasis on clear communication, structured thinking, and measurable impact. Candidates should prepare STAR stories and be ready to discuss process‑improvement projects.

Round 1: Online assessment or phone screen focusing on leadership principles and basic analytics → Round 2: Technical/Leadership interview (scenario‑based questions, data‑driven problem solving) → Round 3: Final HR interview covering cultural fit and compensation.

Amazon promotes a high‑performance culture where ownership and customer obsession are core values. Employees benefit from clear career ladders, frequent feedback, and opportunities to work on large‑scale, impact‑driven projects. While the pace is fast, the company supports flexible work arrangements and invests heavily in employee development.

Graduate (any discipline) with a minimum of 60% aggregate; minimum 4 years of professional experience, including at least 18 months in a team‑lead or managerial role; no active backlogs; flexible to work weekends, nights, and holidays as per shift requirements.

Amazon is one of the world’s most customer‑centric companies, operating across e‑commerce, cloud computing, digital streaming, and artificial intelligence. In India, Amazon has built a massive ecosystem of fulfillment centers, data‑centers, and research labs, offering a fast‑paced environment where innovation meets scale. The company’s culture emphasizes "Learn and Be Curious", "Ownership", and "Customer Obsession", encouraging employees to take bold decisions and deliver measurable impact. Within Amazon, the Global Operations – Artificial Intelligence (GO‑AI) team powers the machine‑learning transformation by providing high‑quality human‑in‑the‑loop data for training sophisticated models used across Amazon’s products. The role of Manager I, Machine Learning Data Ops, is a front‑line leadership position responsible for a team of 20‑25 associates who perform data annotation for ML projects. Reporting to senior operations leadership, the manager will design workforce strategies, ensure SLA compliance, and drive continuous improvement in data quality and productivity. This position blends people‑management with operational analytics, requiring a solid grasp of data handling tools, process optimisation techniques, and stakeholder communication. Key responsibilities include: 1. Lead, motivate and develop a team of 20‑25 data‑annotation associates across virtual and office settings. 2. Ensure daily operational targets (APL, SPL, quality scores) are met while maintaining SLA commitments. 3. Conduct regular 1:1s, performance reviews, and coaching sessions to build a high‑performing culture. 4. Generate and present performance dashboards, SOP updates, and actionable insights to senior leadership. 5. Identify process bottlenecks and implement Lean Six Sigma or other improvement methodologies. 6. Collaborate with internal partners such as ACES, Training, and Quality to launch bar‑raising initiatives. 7. Oversee transition and scaling of 2‑3 annotation programs based on business demand. 8. Pull data from multiple databases (SQL, MySQL) and perform ad‑hoc reporting using advanced Excel. 9. Drive career‑development road‑maps for associates, fostering continuous learning. 10. Maintain 24x7 rotational shift coverage and ensure team adherence to safety and compliance standards. Tech stack and tools: MS Excel (advanced), PowerPoint, SQL/MySQL, data‑annotation platforms, Lean Six Sigma tools, and basic scripting (Python) for data manipulation. The role offers a clear growth path – high performers can progress to Manager II, Senior Manager, or Operations Lead roles within GO‑AI, with exposure to Amazon’s broader AI initiatives. Why join Amazon? Employees gain access to world‑class resources, mentorship from industry leaders, and the chance to impact products used by millions globally. The company’s commitment to diversity, continuous learning, and internal mobility makes it an ideal place for ambitious professionals seeking both leadership experience and technical depth.