Applied Scientist, Minerva

Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning · Software Engineering

In short

We are seeking Applied Scientists to enhance risk detection and response for KDP and Kindle Unlimited. You will develop new models, monitor production models, build datasets, and collaborate with scientists and engineers in Bengaluru and the US. A Master's degree or higher in a related field and 3+ years of model-building experience are required.

Responsibilities

Monitor production risk models and improve their quality, tracking performance and drift.
Design, train, and evaluate improved detection models, taking them to production.
Build and strengthen datasets, labels, evaluation sets, and metrics.
Write clear reports with recommendations for scientists, engineers, and policy partners.

Requirements

3+ years of building models for business application experience
Experience programming in Java, C++, Python or related language
Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Master's degree or above in computer science, computer engineering, or related field

Desired Qualifications

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 LLMs, fine-tuning, embeddings, NLP, computer vision, or multimodal models
Experience with large-scale data processing (for example Spark or SQL on large datasets)
Experience in patents or publications at top-tier peer-reviewed conferences or journals
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++Pythonalgorithmsdata structuresparsingnumerical optimizationdata miningparallel and distributed computinghigh-performance computingSparkSQLLLMsNLPcomputer vision

#risk detection#risk response#model building#model evaluation#production launch#fraud prevention#abuse prevention#copyright infringement#harmful content#text data#image data#behavioral data#data mining#numerical optimization#parallel and distributed computing#high-performance computing#graph-based ML#behavioral modeling#anomaly detection#LLMs#fine-tuning#embeddings#NLP#computer vision#multimodal models#Spark#SQL#generative AI

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