Mid-Level Data Scientist (Data & AI)

Quick Facts

• Role: Mid-level Data Scientist (Data & AI)

Description

Design and deliver data-driven solutions that turn complex enterprise data into actionable insights and intelligent products. Build and deploy machine learning models and advanced analytics pipelines, and leverage GenAI/LLMs to augment data science workflows such as conversational analytics and Text-to-SQL. Collaborate with engineers and architects to translate business requirements into validated approaches and production-ready solutions.

Responsibilities

• Build and deploy ML models and analytical solutions to solve real business problems at enterprise scale

• Implement analytics pipelines from data exploration and feature engineering through training, evaluation, and deployment

• Use GenAI and LLMs for conversational analytics, Text-to-SQL, automated insight generation, and intelligent data exploration

• Convert business requirements into data science methods, validate assumptions, and communicate results to technical and non-technical stakeholders

• Work with cross-functional teams on solution design and technical direction

Benefits

• Permanent employment contract

• People Lead support with a professional development path and optional coaching sessions

• Broad training package (soft/technical/language training, e-learning access, GenAI training, co-financing, certifications)

• Employee Assistance Program (legal, financial, psychological consultations)

• Employee share purchase plan with quarterly dividends if eligible

• Paid employee referral program

• Private medical care and life insurance

• Access to Worksmile platform (including Multisport card)

Requirements

• 3+ years of relevant professional experience in Data Science, Machine Learning, or Data Engineering

• Strong Python and practical PySpark/distributed processing experience

• Solid ML, statistics, and probability foundation with data wrangling and feature engineering

• Experience with at least one ML framework: scikit-learn, XGBoost, PyTorch, or TensorFlow

• MLOps experience including MLflow or Weights & Biases and CI/CD practices for ML models

• Cloud experience (AWS, Azure, or GCP) for deployment and data processing

• Familiarity with LLMs/GenAI and analytical use cases; proficiency with AI-assisted development tools (e.g., Cursor, Copilot)

• Bachelor’s degree or higher in a relevant technical field

• English B2+ proficiency