Data Scientist / AI Research Engineer
Our client is a behavioral health technology company using data science and artificial intelligence to improve mental healthcare outcomes.
In this role, you will partner directly with their clinical team to analyze data, validate AI models, and help write scientific publications.
What You’ll Do
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.
Nice-to-Haves:
Prior experience co-authoring scientific publications or technical whitepapers.
Familiarity with healthcare data privacy standards (like HIPAA).
\n
\n
In this role, you will partner directly with their clinical team to analyze data, validate AI models, and help write scientific publications.
What You’ll Do
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.
Nice-to-Haves:
Prior experience co-authoring scientific publications or technical whitepapers.
Familiarity with healthcare data privacy standards (like HIPAA).
\n
\n