Product Catalog Data Scientist

Quick Facts

• Role: Product Catalog Data Scientist

• Work style: Hybrid model with flexible hours and occasional remote work

Description

You will build statistical models and business rules to improve the quality and correctness of product catalog content and ensure consistency with seller offers. The work covers offline and online modeling and automated tasks such as detecting duplicate products, using both structured and unstructured data. You’ll collaborate closely with data engineers, analysts, PMs, and developers across the full lifecycle—from ideation through implementation.

Responsibilities

• Co-create projects from concept to productization to deliver insights and models that solve real business problems

• Use a wide range of modeling techniques (e.g., gradient boosting, Bayesian methods, causal inference, optimization, deep learning, GenAI/LLMs/agentic AI)

• Work with Data Engineers on data processing pipelines in the GCP environment

• Partner with Analysts, PMs, and Developers to integrate solutions into broader initiatives

• Leverage diverse data types including spatial data, NLP, images, and time series

• Contribute to implementations of both offline and online models

• Participate in brainstorming, active knowledge sharing, and continuous professional development

Requirements

• Hands-on experience with Spark or PySpark

• Practical MLOps & deployment mindset, including automated pipelines and production deployments

• Strong Python software engineering skills (clean OOP code and unit tests)

• Solid statistics and machine learning foundations (model selection, evaluation, and sample size calculations)

• Enthusiasm for GenAI & agentic AI, including prompting/validation knowledge and critical use of coding assistants

• Ability to communicate and translate business challenges into clear ML problems

• Quantitative degree (e.g., Mathematics, Physics, Computer Science, Economics) OR equivalent hands-on experience in ML/Data Science

Benefits

• Flexible working hours in a hybrid model and up to 30 days of occasional remote work

• Long-term discretionary incentive plan via RSUs and an annual performance bonus

• Well-located offices and excellent work tools

• Cafeteria benefits plan (e.g., medical, sports/lunch packages, insurance, vouchers)

• English classes, training budget, hackathons, and an internal learning platform

• Additional day off for volunteering and regular social events

• Modern AI tools and large-scale AI/data initiatives