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Data Science Tech Lead
Не указаноПолная занятостьData ScienceHealthcare TechnologyEnterprise Software
Lead technical research and end-to-end delivery for applied data science projects at a healthcare AI company. Own modeling strategy, client delivery, GenAI workflows, and reusable team tooling while remaining a hands-on senior individual contributor.
Обязанности
- Set the technical research agenda for tabular prediction models and temporal data approaches.
- Define the strategy for extracting value from unstructured data with LLMs, balancing accuracy, cost, latency, and privacy.
- Establish standards for experimentation, evaluation, and model guardrails.
- Own data science delivery across concurrent client engagements from discovery through modeling, validation, and go-live.
- Present results to client stakeholders and coordinate with Solutions, Implementation, and Engineering teams.
- Track delivery risks, blockers, and scope changes across the client portfolio.
- Develop onboarding and delivery playbooks for new GenAI use cases.
- Create reusable agents, skills, and standardized tooling for data science workflows.
- Mentor team members on Claude Code, GenAI practices, and related tools.
Требования
- Master’s degree in computer science, statistics, engineering, or a related quantitative field, or equivalent practical experience.
- At least 6 years of applied data science or machine learning experience, including recent senior individual-contributor technical leadership.
- Experience building or adapting LLM-based machine learning research workflows, monitoring research quality, and conducting peer reviews.
- Strong practical expertise in tabular machine learning, including gradient-boosted trees, feature engineering, and evaluation for regression and classification.
- Experience working with temporal or panel data, including lag features, entity history, and point-in-time snapshots.
- Experience owning client-facing technical engagements from discovery through stakeholder presentations and delivery.
- Hands-on experience building and shipping GenAI applications, including structured extraction, prompt engineering, fine-tuning trade-offs, and agent orchestration.
- Track record of creating reusable tooling, internal libraries, or standardized pipelines that scale team output.
- Strong communication skills with technical, engineering, clinical, and business stakeholders.
- Ability to manage multiple concurrent workstreams and client engagements.
Будет плюсом
- Healthcare, clinical, or EHR data experience, including HL7/FHIR, PHI-aware machine learning, or clinical NLP.
- LLM fine-tuning or distillation experience, including SFT, LoRA, or QLoRA.
- Experience with Claude Code skills, agents, LangChain, LangGraph, or similar agentic tooling.
- Background in causal inference or experimentation design.
- Experience in a scale-up where data science is closely connected to client delivery.
Условия и преимущества
- Work on AI deployed in hospitals and health systems.
- Collaborate with experienced teams and major healthcare partners.
- Influence research direction, product strategy, and roadmap.