Senior Scientist I, Applied Machine Learning and Generative AI, Pharma R&D

Location: Boston, United States

Salary: $140,000–$200,000 for Chicago; $150,000–$210,000 for New York City or San Francisco. The expected range may vary for other locations.

Company

hirify.global develops a precision medicine platform that connects real-world clinical, genomic, and other biomedical evidence to support treatment decisions and pharmaceutical research and development.

What you will do

• Develop algorithms, causal models, and agent-based tools that advance the platform for pharmaceutical research and development.
• Build machine learning and generative AI capabilities using epidemiological, clinical, genomic, transcriptomic, and pathology imaging data.
• Collaborate with Research, Engineering, Data Science, pharma partner science, and clinical teams to deliver computational solutions.
• Analyze pharmaceutical strategies, drug modalities, and pipelines to identify opportunities for platform applications.
• Communicate technical findings and methodologies to clients and other external stakeholders.
• Guide and coach computational biologists and real-world evidence scientists on AI tool adoption.

Requirements

• PhD, or a master's degree with at least 2 years of relevant experience, plus at least 2 additional years of relevant industry or postdoctoral experience.
• Strong quantitative and computational skills in causal AI, causal inference, explainable AI, generative AI, mathematics, or biostatistics, combined with biological, medical, or drug development knowledge.
• Proficiency in R, Python, and SQL, with experience using agentic orchestration frameworks such as LangChain, LangGraph, AutoGen, or DSPy.
• Hands-on experience with causal methodologies, including directed acyclic graphs, counterfactual reasoning, and heterogeneous treatment effect estimation.
• Experience with LLM-driven agent architectures, prompt engineering, retrieval-augmented generation, and function calling or tool use.
• Experience with molecular, clinical trial, or real-world data and a track record of peer-reviewed publications, plus strong communication and client-facing skills.

Nice to have

• Experience integrating multimodal clinical and omics data.
• Strong understanding of data and artificial intelligence in pharmaceutical research and development.
• Understanding of cancer biology.
• Experience with large transcriptome and next-generation sequencing datasets or clinical and real-world medical data.

Culture & Benefits

• Work in a fast-paced environment with shifting priorities.
• Collaborate across a broad data science community and with pharmaceutical partners.
• Benefits may include incentive compensation, restricted stock units, medical benefits, and other company benefits.