Senior Data Scientist – AdTech & Healthcare (f/m/x)

Tired of building yet another chatbot POC? Do you want to train ML models that bring real monetary value and run at a billion-scale? Do you feel like introducing a brand-new probabilistic methodology for a real-world problem?

We build the AI-powered operating system for healthcare marketing - programmatic advertising, data intelligence, patient affordability and AI-enabled engagement for the life sciences industry.

We are looking for a person who knows how to design and implement statistical methods to extract signals from large, messy, real-world datasets and use them for prediction. Everything we build on top of that, from audience reach to specialty insight to measurement, is only as good as your layer.

Your tasks

• Measuring existing model performance and finding ways to improve it

• Owning the design of the new methodology and training the next-gen model for increased predictive power, sample efficiency, and scalability to large datasets

• Extracting unique entities and sparse attributes from heterogeneous sources – both structured and unstructured

• Designing and implementing probabilistic entity linking & matching across multiple large healthcare datasets

• Exploring graph technologies and graph theory

• Building and owning an analytics data store, evaluation, and monitoring pipelines

Requirements

• Minimum 5 years of experience in training and monitoring ML models for production use

• Experience with data and ML engineering, including ML-facing ETL processes over very large datasets and data-quality monitoring

• Expertise in Python data-science and ML libraries like scikit-learn, PyTorch, LightGBM, Splink, spaCy, and Hugging Face

• Practical experience in probabilistic methods for entity linking, matching, imputation, scoring, and evaluation

• Strong command of SQL for distributed warehouses such as Snowflake, BigQuery, or Databricks

• Advanced level of English

Nice-to-have requirements

• Experience with graph technologies such as Neo4j, Amazon Neptune, Stardog, or similar platforms

• Familiarity with Graph Machine Learning concepts, including community detection, Graph Neural Networks (GNNs), and graph embeddings

• Exposure to AI-powered development tools such as Cursor AI, Claude Code, or equivalent solutions

• A proactive mindset with a willingness to take ownership of deliverables and contribute to product direction