Applied Science Manager

Amazon; Asti; Hybrid; EUR 33,000 - 52,000; Full time

Job summary

Amazon Inc. in Bengaluru is building its first science team for Kindle Direct Publishing. You will lead a high-performing group of applied scientists, shaping the science strategy for helping authors publish, grow, and succeed.

You will partner with engineering, product, and policy to translate research into scalable, production-ready models, measure impact on authors, and drive innovations across languages and tools.

Qualifications

• Master's degree or PhD.
• 3+ years of scientists or machine learning engineers management experience.
• 6+ years of building machine learning models or developing algorithms for business applications.
• Experience programming in Java, C++, Python or related language.

Responsibilities

• Hire, develop, and retain a high-performing team of applied scientists across levels, and set its practices for experimentation, evaluation, reviews, publishing, and patents.
• Define the science strategy for helping authors publish, grow, and succeed, balancing near-term launches with long-term bets.
• Partner with engineering, product, and policy teams to take science from prototype to production and measure its impact on authors.
• Write design and planning documents and present options, risks, and tradeoffs to senior leaders.
• Work closely with our scientists in the US to deliver faster for our authors and improve their experience with our services.

Skills

Leadership

Team management

Strategic thinking

Communication

Experimentation

Education

Master's degree

PhD

Tools

Java

C++

Python

Job description

Help authors around the world publish their books, reach readers, and get help when they need it. You'll build and lead our first science team in Bengaluru and shape the assistant authors turn to for answers and the models that keep the Books catalog trustworthy.

Key job responsibilities

• - Hire, develop, and retain a high-performing team of applied scientists across levels, and set its practices for experimentation, evaluation, reviews, publishing, and patents
• - Define the science strategy for how we help authors publish, grow, and succeed, balancing near-term launches with long-term bets
• - Partner with engineering, product, and policy teams to take science from prototype to production and measure its impact on authors
• - Write design and planning documents for your area, and present options, risks, and tradeoffs to senior leaders so they can decide
• - Work closely with our scientists in the US to deliver faster for our authors and improve their experience with our services

About the team

Kindle Direct Publishing (KDP) lets anyone publish a book. We are the science organization for Amazon's Books business, now building our science teams in Bengaluru. Our team works in three connected areas: a support assistant that answers authors' questions from their own account data, in every language KDP supports; multimodal models that use book attributes to keep the catalog trustworthy and help authors reach the right readers; and fine‑tuned models that power the assistant and catalog models with KDP expertise. We serve authors, from first‑time writers to full‑time publishers. We value safety before speed and shared foundations over one‑off fixes. Scientists here get dedicated time for innovation, which can lead to patents and conference papers.

Basic Qualifications

• - Master's degree, or PhD
• - 3+ years of scientists or machine learning engineers management experience
• - 6+ years of building machine learning models or developing algorithms for business application experience
• - Experience programming in Java, C++, Python or related language

Preferred Qualifications

• - Experience building large language model (LLM)-based agents or multimodal generative AI workflows, including tool use, planning, retrieval-augmented generation, or vision-language models
• - Experience fine-tuning or post-training models, including with reinforcement learning
• - Experience with natural language processing (NLP), statistics, data mining, or large-scale retrieval, embeddings, and ranking