Data Scientist / Biostatistician
AI-Driven Biotech & Pharma Equity Research
We are seeking a Data Scientist / Biostatistician to join our biotech and pharmaceutical equity research team. The role will focus on building proprietary AI, machine-learning, and statistical models to predict clinical-trial outcomes and generate differentiated investment insights.
Responsibilities
- Build AI and statistical models to predict clinical-trial results, probability of success, regulatory outcomes, and commercial potential.
- Analyze trial design, endpoints, powering, patient populations, treatment effects, safety, and subgroup data.
- Use generative AI, LLMs, and natural-language processing to analyze scientific publications, regulatory documents, trial registries, company disclosures, and earnings transcripts.
- Create automated tools to track pipelines, clinical catalysts, competitive programs, and emerging risks.
- Convert model outputs and clinical data into clear investment conclusions.
- Work with research analysts on company forecasts, pipeline valuations, and published research.
- Validate models and control for bias, overfitting, data leakage, and AI hallucinations.
Qualifications
- PhD, MD, or master’s degree preferred in biostatistics, statistics, data science, artificial intelligence, epidemiology, bioinformatics, computational biology, medicine, or a related field.
- Strong Python or R skills.
- Experience building machine-learning, predictive, or generative-AI models.
- Strong knowledge of clinical-trial design and statistical analysis.
- Ability to explain complex scientific and quantitative findings clearly.
- Interest in biotechnology, drug development, investing, and financial markets.
- Equity-research experience is helpful but not required.
Primary Location Full Time Salary Range of $135,000 - $165,000.
What they ask for
Required
- PhD, MD, or master’s degree preferred in biostatistics, statistics, data science, artificial intelligence, epidemiology, bioinformatics, computational biology, medicine, or a related field.
- Strong Python or R skills.
- Experience building machine-learning, predictive, or generative-AI models.
- Strong knowledge of clinical-trial design and statistical analysis.
- Ability to explain complex scientific and quantitative findings clearly.
- Interest in biotechnology, drug development, investing, and financial markets.
- Equity-research experience is helpful but not required.