Senior Data Science Engineer

Level: Senior
English: Upper-Intermediate
Employment type: Full-time
Time zone: CET
Start date: ASAP
Duration: 12+ months


Project Overview

You’ll join an ongoing project focused on developing an advanced Recommendation System powered by AI and machine learning. The goal is to deliver highly personalized user experiences through data-driven insights. The system analyzes large volumes of data to suggest relevant content and integrates seamlessly across multiple platforms, providing real-time, context-aware recommendations.


Team setup: 2 Python Developers, 1 Data Scientist, and 1 Project Manager


Soft Skills

  • Excellent communication and problem-solving skills
  • Ability to work independently with minimal supervision
  • Confidence in clarifying and refining requirements directly with stakeholders


Hard Skills / Must Have

  • 5+ years of professional experience in Data Science or related fields
  • Solid expertise with Scikit-learn for data manipulation, model development, and evaluation
  • Experience working with laser embeddings to generate text-based content representations
  • Strong background in developing and enhancing recommendation models based on content similarity
  • Proven experience in clustering models and optimizing clustering algorithms and workflows


Key Responsibilities

  • Analyze and enhance recommendation models based on content similarity
  • Develop and optimize clustering models and their performance
  • Improve the process of building similarity matrices
  • Refine data structures to achieve better recommendation accuracy


Tech Stack

Flask, Pydantic, Gunicorn, Peewee, PostgreSQL, Sentry, Elastic, Scikit-learn, laser embeddings, concurrency and multithreading


Interview Process

  1. English check — 15 minutes
  2. Internal technical interview — 1–1.5 hours
  3. Final technical interview with the client — 1 hour

Required languages

English B2 - Upper Intermediate
Flask, Pydantic, Gunicorn, Peewee, PostgreSQL, Sentry, Elastic, Scikit-learn, laser embeddings, concurrency and multithreading
Published 22 October
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2 applications
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