Data Scientist / AI Research Engineer

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JetBridge AI

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

🤖 Artificial Intelligence

🤝 B2B

🎯 Recruiter

💰 $600k Seed Round - JetBridge on 2018-02

Artificial Intelligence • B2B • Recruitment

JetBridge AI is an AI transformation partner that provides senior AI/ML engineering teams, technical leadership, and AI infrastructure to VC-backed startups and enterprises. They build production-grade AI/ML products, recruit and place engineering talent, run pilot-to-rollout engagements, and offer consulting and research in areas like NLP, genomics, predictive analytics, and GPU-optimized model training. Clients span biotech, healthcare, finance, CX, manufacturing, logistics, and SaaS, and the company emphasizes delivering measurable ROI through embedded engineering teams and AI-driven systems.

📋 Description

• Design, build, and maintain data pipelines to analyze complex behavioral health datasets. • Implement and fine-tune AI/ML models to identify clinical trends and predictive patterns. • Partner closely with the clinical team to explore clinical hypotheses, running the data queries and statistical analyses needed to prove or disprove them. • Act as the technical voice on scientific papers, abstracts, and whitepapers. You will translate data methodologies, model architectures, and statistical findings into clear, publication-ready text. • Turn raw data into compelling visualizations and narratives that can be easily understood by both medical professionals and the broader public.

🎯 Requirements

• Strong proficiency in Python or R, and deep experience with data science/data engineering libraries (e.g., Pandas, NumPy, Scikit-Learn) and AI/ML frameworks (TensorFlow, PyTorch, or LLM orchestration tools). • Solid experience cleaning, structuring, and analyzing messy, real-world datasets (healthcare or behavioral data is a plus, but not required). • An ability to explain complex technical and statistical concepts clearly on paper. You should genuinely enjoy the process of writing and documenting your findings. • Experience developing and deploying machine learning models, with specific experience in NLP, LLMs, or predictive modeling. • Prior experience co-authoring scientific publications or technical whitepapers. • Familiarity with healthcare data privacy standards (like HIPAA).