[Hiring] Senior Data Science/Machine Learning Engineer @Poland and Central-Eastern Europe

Role Description

You will be:

• Owning models end to end across caller identity, business identification, spam and fraud detection: problem framing, data, training, evaluation, deployment and monitoring.
• Working on ranking, classification, graph, personalisation and LLM-based models.
• Setting the team’s evaluation standards through historical backtests, honest baselines, false-positive checks on legitimate callers, shadow runs and A/B tests.
• Building shared tooling that makes good practice the easy path, including reusable evaluation harnesses, experiment tracking, project templates and workflows for moving work from notebooks into production code.
• Working with product stakeholders to turn loosely defined problems into clear modelling goals and success metrics, and explaining results and trade-offs to non-technical audiences.
• Sharing responsibility for model monitoring and incident handling, including drift detection.
• Documenting decisions and results in short, reproducible analyses.

Qualifications

• Several years of hands-on experience building and shipping machine-learning models to production.
• Strong Python and SQL skills, with experience working with large-scale data in BigQuery or a similar data warehouse.
• Experience with classical machine learning, such as gradient boosting and ranking, and deep learning using TensorFlow/Keras or PyTorch, including training on GPUs.
• Experience designing offline evaluations that predicted online results, and running A/B tests.
• Experience with data and ML pipelines using Airflow, dbt and Spark, or with running training jobs on Kubernetes.
• Work from the European Union region and a work permit are required.

Requirements

• Experience gathering requirements from non-technical stakeholders, turning them into a scoped plan, and communicating results and trade-offs clearly.
• Experience setting or improving data science standards within a team, including evaluation, reviews and reproducibility.
• Experience with graph machine learning, such as GNNs, link prediction or label propagation, or with recommendation and retrieval models, such as two-tower models and embeddings.
• Experience using LLMs in production for extraction, classification or generation, including LLM-as-judge evaluation.
• Familiarity with MLOps tools such as MLflow for experiment tracking, model registries, versioning and reproducible training.
• Experience with NLP on short, multilingual or transliterated text, such as names, or computer vision on user images.
• Experience with telecom or calling data, or in fraud detection.

Recruitment Process

• CV review
• HR call
• Interview
• Client Interview
• Decision