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.
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Measuring existing model performance and finding ways to improve it
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Owning the design of the new methodology and training the next-gen model for increased predictive power, sample efficiency, and scalability to large datasets
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Extracting unique entities and sparse attributes from heterogeneous sources – both structured and unstructured
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Designing and implementing probabilistic entity linking & matching across multiple large healthcare datasets
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Exploring graph technologies and graph theory
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Building and owning an analytics data store, evaluation, and monitoring pipelines
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Minimum 5 years of experience in training and monitoring ML models for production use
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Experience with data and ML engineering, including ML-facing ETL processes over very large datasets and data-quality monitoring
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Expertise in Python data-science and ML libraries like scikit-learn, PyTorch, LightGBM, Splink, spaCy, and Hugging Face
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Practical experience in probabilistic methods for entity linking, matching, imputation, scoring, and evaluation
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Strong command of SQL for distributed warehouses such as Snowflake, BigQuery, or Databricks
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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
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