Applied LLM / Data Scientist

Overview

Synagen builds specialized AI agents for healthcare and oncology, designed to support complex clinical decisions and biomedical workflows with actionable, high-precision outputs. We combine modern AI with clinical expertise to create software that integrates into real provider environments and delivers value in practice.

As our Applied LLM / Data Scientist, you will help turn high-volume patient and clinical data into scientific, research, and clinical insights—by building the data and model operations layer that makes this reliable, scalable, and compliant. A core part of this role is applied research and analytics delivery with pharma partners : you will work customer-near on real projects, translate scientific questions into data products and agent workflows, and ship outcomes that can be used in practice.

What you will do

• Lead applied research / analytics projects with pharma and clinical partners: independently scope questions, define datasets and success criteria, and deliver end-to-end outputs with medical stakeholders.
• Build and operate scalable pipelines that transform raw clinical/patient data into structured, queryable, analysis-ready datasets.
• Design and evolve a datalake / lakehouse approach on Azure (storage, compute patterns, governance, access controls).
• Develop and maintain ontologies / terminology mappings and a consistent internal data model to enable reliable downstream analytics and agent reasoning.
• Build “SynInsight”-style data products for partners (e.g., cohorts, endpoints, phenotypes, evidence-ready exports and reports) that are robust, reproducible, and measurable.
• Implement LLM/agent operations: prompt/workflow versioning, evaluation harnesses, monitoring, regression testing, and cost/performance controls—using AI-assisted development tools (e.g., Claude Code, Codex) where helpful.
• Build agents that automate R&D workflows (e.g., data-to-cohort pipelines, evidence synthesis, structured insight generation), and operationalize them with proper evaluation and monitoring.
• Drive privacy-preserving data capabilities, including synthetic data generation for development, evaluation, and safer sharing/testing in projects (including Azure-based implementations).
• Ensure security, privacy, and compliance expectations are met when processing sensitive healthcare data in Germany/EU and the US (e.g., GDPR, ISO 27001, SOC 2, BSI C5; US healthcare compliance alignment).

Qualifications

• Strong experience in applied data science / ML engineering / MLOps , ideally in pharma, R&D, or healthcare-adjacent environments.
• Proven ability to build production-grade pipelines for messy real-world data (ETL/ELT, data quality, lineage, reproducibility).
• Experience building and operating LLM/agent systems in production (workflows, evaluation, monitoring, reliability).
• Strong coding skills (Python + SQL) and comfort with engineering best practices (tests, CI/CD, documentation).
• Practical experience structuring data with ontologies/terminologies and making it usable for analytics and downstream systems.
• Experience in AI-assisted programming (Claude Code, Codex, etc.)
• Fluent in English (written and spoken).

Good to have

• Experience with clinical terminologies and standards (e.g., ICD-10, SNOMED CT, LOINC, RxNorm/ATC).
• Experience with modern data stack components (lakehouse patterns, columnar formats, distributed compute) on Azure.
• Familiarity with privacy-preserving data processing (pseudonymization/de-identification, access partitioning, audit trails).
• Experience delivering customer-facing data/ML projects end-to-end.
• Experience with modern DataOps tooling for reproducible data and agent workflows (e.g. dbt, Dagster, or similar asset-based orchestration and transformation frameworks).

Impact

Real-world impact in oncology: build integrations that bring AI into clinical workflows where accuracy and trust matter. High ownership: you will shape our interoperability layer end-to-end and define how we integrate at scale.