Must have

• Data science

• Machine learning

• Deep learning

• Python

• scikit-learn

• SQL

• PyTorch

• MLOps

• CI/CD

• Communication skills

• English (B2)

Requirements description

Ideally you have:

• 3+ years in Data Science role
• Advanced knowledge of Machine and Deep Learning algorithms, Statistics and Mathematics
• Experience of working in Python, including Scikit-learn, PyTorch and common machine learning packages
• Good knowledge of SQL
• Understanding data preprocessing, feature engineering, and model evaluation techniques
• Exposure to MLOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc. are a plus
• Familiarity with Agile methodologies and working in cross-functional teams
• Strong analytical thinking, problem-solving skills, and attention to detail
• Excellent communication and teamwork skills
• Strong verbal and written English communication skills

Nice to have:

• Experience in large language models and prompt engineering
• Experience in agents design and orchestration

Offer description

Job overview

Godel is looking for talented Data Scientist to join one of our teams engaged in development of business applications for major British companies. We always have new and exciting projects with opportunities to improve your skills in a wide spectrum of technologies related to ML/AI. If you are an enthusiastic and ambitious specialist, willing to take active participation in shaping a product, this job is for you!

We expect candidates to be located in one of the following cities: Warsaw, Wroclaw, Lodz, Gdansk or Bialystok.

Ranges

EC 10 000 - 23 000 PLN gross/month

B2B 80 - 145 PLN / hour

Your responsibilities

1. Analyze large historical datasets to extract meaningful features
2. Develop and validate machine learning models for predictive analytics
3. Prepare and preprocess data for modeling, ensuring data quality and consistency
4. Define evaluation metrics (precision, recall, false positive rate, etc.) and align them with project success criteria
5. Conduct experiments, document results, and manage reproducible training pipelines
6. Collaborate with architects and engineers to integrate the model into a lightweight demonstrator or prototype application

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