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REQUIREMENT_ID_1424 • 3-DAY_ACTIVE_POLICY

Manager I, Machine Learning Data Ops

Company
Company Amazon
Type
Opportunity Type Internship
Salary
Stipend / Salary ₹20-25 LPA
Location
Location Karnataka
Posted Date
Posted Date Sep 30, 2026
Leadership Team Management Data Analysis Advanced MS Excel PowerPoint SQL/MySQL Lean Six Sigma Process Improvement Communication Coaching
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Amazon Placement Papers – General Questions Open Resource ↗
Covers typical aptitude and reasoning questions asked in Amazon recruitment, helping candidates prepare for the initial screening.
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Amazon Recruitment Process Experiences Open Resource ↗
First‑hand interview experiences from candidates, offering insights into round formats and question types.
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Amazon Interview Preparation Guide Open Resource ↗
Comprehensive guide on Amazon's interview stages, leadership principles, and preparation strategies.
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Algorithm Practice Problems Open Resource ↗
Curated problem set to sharpen coding and problem‑solving skills, useful for technical rounds at Amazon.

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.

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

Amazon — Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource ↗
COMPANY GUIDE 🎯
Amazon Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Amazon and off-campus tech roles.

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource ↗
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Leadership, Team Management, Data Analysis, Advanced MS Excel, PowerPoint, SQL/MySQL, Lean Six Sigma, Process Improvement, Communication, Coaching) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Amazon Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer ▼
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer ▼
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer ▼
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer ▼
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer ▼
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join Amazon as a Manager I, Machine Learning Data Ops?
Preparation Tip: Highlight Amazon's market reputation, recent tech innovations, and how your skills in Leadership directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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