Job Title: Senior AI / Data Scientist – Healthcare / Life Sciences
Location: [Remote]
Employment Type: Full-time - Contract
About the Role
We are looking for a hands-on Senior AI / Data Scientist with deep experience in biology/healthcare/clinical research to design, build, and deploy data science and GenAI solutions. This role sits at the intersection of advanced statistical modeling, modern ML/LLMs, and stakeholder-facing consulting. You’ll work closely with clinical, product, and business teams to translate complex methods into actionable, high-impact outcomes.
Key Responsibilities
- Lead end-to-end data science projects in the healthcare / life sciences domain (problem framing → data strategy → modeling → validation → business adoption).
- Build and evaluate classical AI/ML models (regression, classification, causal inference frameworks, time-to-event/survival models, Bayesian approaches) for clinical and real-world datasets.
- Design and implement LLM-based solutions (content summarization, content generation, RAG architectures) for medical/scientific content and knowledge bases.
- Experiment with and operationalize agentic/workflow-based architectures (e.g. LangChain or similar) to orchestrate complex multi-step tasks.
- Partner with clinicians, SMEs, and business stakeholders to clarify requirements and ensure models are interpretable, robust, and compliant.
- Present methods, findings, and recommendations to senior/non-technical stakeholders in clear, business-oriented language.
- Document approaches and contribute to internal best practices, reusable components, and accelerators.
Required Qualifications
- PhD (preferred) or Master’s in Biostatistics, Computational Biology, Statistics, Machine Learning, Computer Science (with healthcare focus), or related quantitative field
- .4–5+ years of experience working with data in biology, healthcare, pharma, clinical research, or life sciences environments
- .Strong applied experience with classical AI / statistical modeling (GLM, mixed models, survival analysis, causal inference / causal impact, hypothesis testing)
- .Solid experience building and deploying ML and Deep Learning models on real-world datasets
- .Hands-on experience with LLMs for tasks such as summarization, generation, retrieval-augmented generation (RAG), and evaluation
- .Experience or strong familiarity with agentic / tool-using / LangChain-like architectures
- .Proficiency in Python and common DS/ML libraries (pandas, scikit-learn, PyTorch/TensorFlow, statsmodels); familiarity with vector DBs is a plus
- .Demonstrated ability to explain complex DS/ML methods to non-technical audiences and present to senior stakeholders
- .Consulting mindset – able to work independently, manage multiple stakeholders, and turn ambiguous business problems into structured analytical work
Preferred Qualifications
- Experience with clinical trial data, RWD/RWE, HL7/FHIR, or medical ontologies (SNOMED, ICD)
- Experience working in regulated / compliance-heavy settings
- Experience building evaluation frameworks for LLM/agentic systems
What We’re Looking For
- Someone who is as comfortable coding as they are presenting.
- Someone who can balance scientific rigor with business impact.
- Someone who can work independently but collaborate across product, clinical, and engineering teams.
