Applied Scientist, Minerva

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science · Data Analysis

In short

We are looking for Applied Scientists to advance how Minerva detects and responds to risk across KDP and Kindle Unlimited, building new models and raising the bar on the ones in production. You will own a problem end to end, from framing and modeling through evaluation and production launch, working with scientists and engineers in Bengaluru and the US. Key responsibilities include monitoring production risk models, improving their quality, and designing, training, and evaluating new detection models. The role also involves building and strengthening datasets, labels, evaluation sets, and metrics, and writing clear readouts for various partners.

Responsibilities

Monitor production risk models and push their quality higher: track performance and drift, improve how performance is measured, and lead the deep-dive science that turns model behavior into better detection.
Design, train, and evaluate improved detection models, and take them to production with engineering partners.
Build datasets, labels, evaluation sets, and metrics, and strengthen them as abuse patterns change.
Write clear readouts with recommendations for scientists, engineers, and policy partners.

Requirements

3+ years of building models for business application experience
PhD, or a Master's degree and experience in CS, CE, ML or related field research
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Experience programming in Java, C++, Python or related language

Desired Qualifications

Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience with fraud detection, abuse prevention, trust and safety, or content moderation
Experience with model monitoring, data drift detection, or calibration
Experience with graph-based ML, behavioral modeling, or anomaly detection
Experience with large-scale data processing (for example Spark or SQL on large datasets)
Demonstrated experience leveraging generative AI tools to enhance workflow efficiency and productivity, with the ability to craft effective prompts and critically evaluate AI-generated outputs in a professional setting

Skills

JavaC++PythonSparkSQLMachine Learning

#Risk Management#Machine Learning#Data Science#Fraud Detection#Abuse Prevention

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