Data Scientist

Job Description Roles & Responsibilities The Data Scientist turns CCAD s clinical, operational, imaging, research, digital-front-door and device data into evidence-based insights and validated AI solutions that advance patient care, quality, safety, efficiency, and discovery. The role leads problem framing, cohort and feature design, statistical and causal analysis, experimentation, predictive modelling, computer vision and multimodal AI evaluation, and clear communication of findings to clinical and business stakeholders. As CCAD advances toward the north star of an autonomous hospital, the Data Scientist partners with clinicians, clinical informatics, data engineering, AI/ML engineering, governance, and product teams to convert high-value use cases into safe, measurable, human-in-the-loop analytics and AI products . What You ll Do: Primary Job Function: Partner with clinical, operational and digital teams to prioritize AI use cases aligned to autonomous hospital goals such as patient flow, capacity, staffing, demand forecasting, diagnostics, clinical decision support and command-center automation. Translate clinical and operational problems into measurable hypotheses, data requirements, benefits cases, user stories, success metrics and safe human-in-the-loop workflows. Create analytic, validation and adoption plans with clinicians, product owners and governance stakeholders. Healthcare Data Understanding & Feature Engineering: Extract, profile, cleanse and validate structured, semi-structured and unstructured data from EHR, ERP, scheduling, labs, pharmacy, PACS/RIS, DICOM, devices/IoMT, patient experience, contact-center and digital channels. Build clinically meaningful cohorts and features using Epic Clarity/Caboodle/Cogito, FHIR, HL7, DICOM/DICOMweb, OMOP and terminology standards such as ICD, CPT, SNOMED CT, LOINC and RxNorm where relevant. Apply data-quality rules, de-identification, missing-data handling, imputation, leakage checks, normalization, NLP extraction and reproducible feature documentation. Statistical, Causal & Experimental Analysis: Conduct EDA, hypothesis testing, regression, Bayesian analysis, survival/time-to-event analysis, time-series forecasting, experimental design, A/B testing and quasi-experimental impact evaluation. Analyze subgroup performance, equity/fairness, calibration, clinical utility, operational ROI and uncertainty to support safe decisions. Produce clear narratives, visuals and recommendations for technical and nontechnical audiences, including clinical leaders and executives. Model Development & Validation Develop and evaluate supervised, unsupervised, semi-supervised, NLP, forecasting, optimization, simulation and reinforcement-learning models using modern open-source and cloud ML frameworks. Establish baselines, train/validation/test splits, temporal validation, cross-validation, hyperparameter tuning, error analysis, explainability and confidence intervals. Prepare champion-model documentation, model cards, validation packs, operating thresholds, limitations, expected failure modes and production handoff materials. Computer Vision & Multimodal AI: Build, fine-tune or evaluate computer-vision models for medical imaging, digital pathology, video enabled operations, document images and multimodal clinical workflows. Apply CNNs, U-Net architectures, Vision Transformers, object detection, segmentation, classification, MONAI, OpenCV, DICOM/DICOMweb, multimodal LLMs and vision-language models as appropriate. Evaluate clinical and operational usefulness using metrics such as sensitivity, specificity, AUROC/AUPRC, Dice/IoU, calibration, turnaround time, false-alert burden and human-review requirements. Generative AI, LLMs & Agentic Workflows: Prototype and evaluate RAG, summarization, information extraction, classification, conversational analytics, clinical documentation support and agentic workflow use cases. * Design prompt, retrieval and tool-use evaluation sets; test hallucination, grounding, bias, privacy, prompt-injection and safety risks. Work with AI/ML Engineers to define structured outputs, guardrails, approved knowledge sources, monitoring measures and deployment criteria. Visualization, Decision Support & Storytelling: Build dashboards, analytical applications, what-if simulators and command-center decision aids using Power BI, Tableau, Streamlit, Shiny, Dash, Power Apps or equivalent tools. Design visualizations and explainability views that make model output interpretable and actionable for clinicians, operational leaders and frontline teams. Measure adoption, user feedback, workflow fit and benefits realization after deployment. Responsible Clinical AI Governance: Apply privacy, data governance, clinical safety, responsible AI, human oversight, change control, auditability and cybersecurity requirements throughout the model lifecycle. Create and maintain model cards, data sheets, validation summaries, monitoring thresholds, fairness assessments and retraining recommendations. Participate in AI governance, safety, privacy and clinical validation reviews before production use. Collaboration & Agile Delivery: Work in Agile squads with clinicians, product owners, data engineers, AI/ML engineers, BI developers and governance teams to deliver measurable outcomes. Write reusable, modular, version-controlled analysis and modeling code with peer review and reproducibility practices. Support production analysis, incident investigation and after-hours validation when changes affect critical analytics or AI services. What You ll Bring: Expert Python and SQL; strong R preferred. Deep hands-on use of Pandas, NumPy, Polars, PySpark/Spark SQL, scikit-learn, statsmodels and notebook-to-production development practices. Strong statistical foundation: probability, Bayesian inference, regression, causal inference, survival analysis, time-series forecasting, experiment design, sample-size estimation, power analysis and uncertainty quantification. Advanced ML expertise across supervised, unsupervised, NLP, forecasting, optimization, simulation and reinforcement-learning methods, including appropriate evaluation metrics and clinical interpretation. Computer vision expertise for healthcare and operations: DICOM/DICOMweb, PACS/RIS patterns, CNNs, U-Net, Vision Transformers, segmentation, object detection, digital pathology, medical imaging, video analytics and multimodal/vision-language models. Generative AI expertise: LLM evaluation, prompt design, RAG, embeddings, vector search, knowledge graphs, structured outputs, agentic workflow design, hallucination/grounding measurement and safe human-in-the-loop design. Desired Candidate Profile Bachelor degree in Statistics, Data Science, Computer Science, Mathematics, Physics, Biomedical Engineering, Epidemiology, Health Informatics, Health Economics or a related quantitative field. PREFERRED: Master degree or PhD in a relevant quantitative, AI, biomedical, clinical informatics or health-data discipline. 3+ years in data science, advanced analytics, applied ML/AI or applied research, including healthcare, life sciences, hospital operations or similarly regulated data environments. 5+ years in healthcare AI/analytics; experience leading AI products from problem definition through validation and adoption; peer-reviewed research or clinical quality improvement experience. Company Industry IT - Software Services Department / Functional Area IT Software Keywords Data Scientist Get real-time job updates only on our App