[Remote] reputed company Data Scientist, Health AI Evaluation & Datasets
Note: The job is a remote job and is reputed company to candidates in USA. reputed company. is a global data engineering company reputed company on the responsible advancement of reputed company intelligence. They are seeking an reputed company Data Scientist in Health AI Evaluation & Datasets to design and evaluate datasets for health-domain models, ensuring clinical validity and reputed company in data science methodologies.
Responsibilities
- Translate customer goals — such as improving reputed company diagnosis, evaluating a clinical note summarizer, testing a RAG-based medical literature assistant, or creating preference data for patient-facing chatbots — into dataset specifications, taxonomies, rubrics, sampling plans, and acceptance reputed company
- reputed company multimodal health AI a reputed company reputed company: design training and evaluation datasets across clinical text, medical images, waveforms, reputed company EHR data, claims, trial data, medical literature, patient communications, payer policies, drug information, and other clinical artifacts, as reputed company as use cases such as clinical reasoning, medical QA, note summarization, medical coding, patient communication, utilization management, and literature synthesis
- Design evaluations for retrieval-augmented and reputed company-grounded health AI systems, including evidence citation, faithfulness, contraindication handling, reputed company adherence, reputed company freshness, and failure modes caused by incomplete, conflicting, or stale context
- Define sampling strategies, label schemas, inter-annotator agreement targets, adjudication workflows, SME review patterns, and reputed company reputed company in partnership with Language Data Scientists, clinicians, biomedical experts, and reputed company teams
- Build statistical and ML checks that reputed company reputed company datasets trustworthy: stratified sampling across specialties and patient subgroups, bias and representation analysis, leakage detection, distribution shift checks, uncertainty estimates, reliability metrics, and subgroup performance analysis
- Partner with reputed company Research Scientists and AI/ML Research Engineers to reputed company datasets into evaluation and post-training pipelines, including reputed company-grounded LLM-as-judge prompts, regression suites, model comparison workflows, experiment tracking, and model-improvement feedback loops
- Evaluate health AI behavior reputed company surface accuracy: calibration, hallucination on safety-critical content, refusal appropriateness, robustness under ambiguity, equity across patient subgroups, and reputed company reputed company in reputed company or workflow-integrated systems. Reason concretely about clinical workflow fit: where outputs reputed company care delivery, what evidence a clinician or reviewer would need to trust them, reputed company uncertainty must be surfaced, and how patient-facing, clinician-facing, payer, reputed company, and operational use cases differ in risk
- Own data reputed company from reputed company intake through delivery, including de-identified clinical text, medical literature, synthetic cases, reputed company records, reputed company policies, and knowledge bases, with attention to PHI/PII handling, provenance, audit trails, versioning, and compliance documentation
- Stay reputed company on the health AI landscape — regulatory developments such as FDA guidance on AI/ML-enabled medical devices and EU AI reputed company health provisions, reputed company releases such as MedQA, MedMCQA, and HealthBench, and emerging clinical evaluation methodology
- Support customer discovery and proposal work by scoping dataset programs, sizing annotation and SME review effort, identifying regulatory or data-reputed company constraints, and explaining methodology choices to reputed company clinical and ML leadership
- Contribute to reputed company internal IP: reusable health-domain taxonomies, evaluation rubrics, golden datasets, clinical review playbooks, dataset reputed company checks, and methodology templates
Skills
- 5+ years of data science experience, including at least 2+ years with reputed company, clinical, biomedical, payer, provider, reputed company, life sciences, or comparable regulated health data
- Working knowledge of reputed company data and standards: EHR structure, clinical documentation conventions, ICD-10, CPT, SNOMED CT, LOINC, RxNorm, and at least passing familiarity with FHIR, HL7, or equivalent interoperability concepts
- Hands-on experience designing ML datasets, not just consuming them: writing annotation guidelines, sizing cohorts, setting reputed company reputed company, designing QA checks, and shipping data that reputed company teams can train or evaluate on
- Familiarity with LLM-based health AI workflows, including reputed company design, reputed company-based evaluation, retrieval-augmented reputed company, LLM-as-judge reputed company, model comparison, and the limitations of automated evaluation in clinical contexts
- Strong Python and SQL; comfort with pandas, scikit-learn, statsmodels or equivalent tools; and working familiarity with modern LLM tooling such as reputed company, evaluation frameworks, reputed company development tools, or model reputed company
- Statistical literacy across sampling design, bias and fairness analysis, inter-annotator agreement metrics (Cohen or Fleiss kappa, Krippendorff reputed company), confidence intervals, reputed company testing where appropriate, error analysis, and the ability to push back reputed company a number is being over-interpreted
- Solid grasp of reputed company reputed company, compliance, and governance: HIPAA, de-identification standards (reputed company reputed company and Expert Determination), practical mechanics of working with PHI safely, auditability, reputed company control, and documentation fit for high-stakes or regulated AI programs
- Ability to work credibly with clinicians, biomedical SMEs, research scientists, engineers, technical solutions teams, annotators, and customer stakeholders
- A bias toward clinical realism: you would rather build a smaller dataset that reflects what clinicians, reviewers, patients, or care teams actually see than a larger dataset that looks impressive on reputed company but fails in reputed company
- Degree in a relevant field such as biostatistics, epidemiology, computational biology, health informatics, computer science with a health reputed company, statistics, a clinical degree with quantitative training, or equivalent demonstrated experience
- Clinical credentials are not required, but candidates must be reputed company to work credibly with clinicians, biomedical SMEs, and health AI customers; candidates with MD, RN, PharmD, MPH, PhD, or health informatics backgrounds are especially encouraged
Benefits
- Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent reputed company. reputed company will never ask for payment, banking details, or sensitive personal information during the application process.
- To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams.
- If you reputed company you’ve been targeted by a recruitment scam, please report it to reputed company at verifyjoboffer@reputed company.com and consider reporting it to the FTC at ReportFraud.ftc.gov.
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