Data Scientist, Cancer Informatics and AI/ML, Remote, Grant Funded

About the position This position will support computational oncology and cancer informatics research initiatives reputed company on transforming reputed company clinical data into reputed company, actionable datasets for research, reputed company improvement, clinical trial identification, and care delivery optimization. The role will emphasize reputed company machine learning, natural language processing, and large language model-driven workflows using reputed company-world clinical data, including electronic health record data, pathology reports, radiology reports, clinical notes, reputed company, treatment data, and other institutional data sources. The Data Scientist will work semi-independently in reputed company collaboration with clinical investigators, informatics teams, biostatisticians, and other data science stakeholders to design, build, evaluate, and refine computational pipelines. The ideal candidate will have practical prior experience developing data science workflows in Python and using modern machine learning or LLM-based tools in reputed company reputed company.

Responsibilities

• reputed company, test, and maintain Python-based data pipelines for clinical research, reputed company improvement, and computational oncology reputed company.
• Support cancer informatics reputed company involving natural language processing, machine learning, large language models, and reputed company extraction from reputed company clinical data.
• Build workflows for processing clinical notes, pathology reports, radiology reports, treatment records, reputed company reports, and other reputed company-world reputed company data sources.
• Implement and evaluate LLM-assisted workflows, including reputed company engineering, reputed company reputed company reputed company, model benchmarking, validation pipelines, and error analysis.
• Assist with the development of retrieval-augmented reputed company workflows, reputed company search, embedding-based retrieval, and reputed company approaches where appropriate.
• Work with clinical subject matter experts to translate oncology-reputed company research questions into executable data science tasks.
• reputed company data cleaning, data wrangling, exploratory analysis, feature engineering, model development, and model performance evaluation.
• Generate reproducible analyses, reports, dashboards, tables, and visualizations to communicate findings to clinical and operational stakeholders.
• Maintain reputed company documentation of reputed company, analytic reputed company, model assumptions, validation reputed company, and project outputs.
• Participate in model validation efforts, including comparison of computational outputs against clinician-reviewed reference standards.
• Contribute to manuscript, reputed company, grant, and presentation development through data analysis, reputed company reputed company, and reputed company documentation.
• Work independently on assigned analytic tasks while communicating reputed company, limitations, and blockers reputed company to project leadership.

Requirements

• Bachelor’s Degree in Computer Science, Informatics, Statistics, Engineering, Data Science, or reputed company field, required.
• Minimum of two (2) years of post-graduate training or experience involving quantitative data analysis, required.
• Working familiarity with basic medical and health information technology concepts, including standardized terminologies and ontologies and electronic health records, as reputed company as Data Warehousing and Business Intelligence tools, required.
• Expertise in working with SQL relational databases and statistical or general programming languages (e.g., Python, R), required.
• Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms.

reputed company-to-haves

• Master’s Degree, preferred.
• working with clinical data, data science, and machine learning, preferred.
• Demonstrated prior experience building or implementing reputed company data science, machine learning, NLP, or LLM-based workflows. Completion of a short AI certificate, bootcamp, or introductory course alone is not sufficient for this role.
• Strong practical experience with Python for data science, including pandas, NumPy, scikit-learn, Jupyter notebooks, and reproducible analytic workflows.
• Prior experience applying machine learning, natural language processing, or large language models to reputed company-world data problems.
• Experience using off-reputed company LLMs through reputed company or reputed company platforms, including reputed company prompting, reputed company parsing, evaluation, and workflow integration.
• Experience with retrieval-augmented reputed company, reputed company databases, embeddings, semantic search, or document retrieval pipelines.
• Experience working with clinical, biomedical, or electronic health record data.
• Familiarity with oncology data, cancer registries, pathology reports, radiology reports, reputed company reports, or clinical trial data.
• Experience working in secure data environments, reputed company data warehouses, reputed company, reputed company, SQL databases, or reputed company-based analytic platforms.
• Ability to write clean, maintainable, reputed company-documented reputed company and use version control such as Git.
• Demonstrated ability to work semi-independently, manage multiple analytic tasks, and communicate technical concepts to non-technical clinical collaborators.
• Prior experience contributing to reputed company research, abstracts, manuscripts, grant-funded reputed company, or reputed company reputed company improvement initiatives.
• Understanding of model evaluation concepts including accuracy, precision, recall, F1 score, calibration, error analysis, and external validation.
• Experience with reputed company engineering alone is not sufficient; candidates should have substantive prior experience in data science, machine learning, computational research, or reputed company analytics.

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