Dataset Lead, Clinical Medicine

Reinforced Learning; Buffalo (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Reinforced Learning in Buffalo, NY seeks a clinician-leader to drive programs around diagnostic, treatment-planning and documentation tasks. You will design and oversee the review process ensuring data privacy and safe learning from real clinical datasets.

You will collaborate with physicians, nurses, pharmacists and other professionals to encode standards, create robust rubrics, and manage adjudication for contested cases while maintaining privacy controls on data leaving the facility.

Qualifications

• MD/DO/NP/PharmD or equivalent required and recent clinical practice.
• Experience in quality improvement, clinical education, guideline development or research.
• Clear writing and comfort making judgement calls on ambiguous cases.
• Interest in how models reason about medicine and where they shouldn’t be trusted.

Responsibilities

• Lead programs where clinicians create diagnostic, treatment-planning and documentation tasks.
• Design the review process that keeps the data safe to learn from.
• Coordinate a contributor bench of practising physicians, nurses and allied professionals.
• Develop rubrics that encode clinical standards and flag unsafe reasoning.
• Oversee multi-reviewer adjudication for contested cases.
• The privacy check every clinical dataset passes before it leaves the building.

Skills

Clear writing

Judgement in ambiguity

Interest in model reasoning

Quality improvement

Guideline development

Clinical communication

Education

MD/DO/NP/PharmD or equivalent

Job description

The role

Medical reasoning carries real consequences and is hard to grade. You’ll lead the programs where clinicians create diagnostic, treatment-planning and documentation tasks, and design the review process that keeps the data safe to learn from.

What you’ll own

• Task families defined with partner research teams: differential diagnosis, guideline application, chart review, patient communication.
• A contributor bench of practising physicians, nurses, pharmacists and allied professionals.
• Rubrics that encode clinical standards and flag unsafe reasoning.
• Multi-reviewer adjudication for contested cases.
• The privacy check every clinical dataset passes before it leaves the building.

What you bring

• MD, DO, NP, PharmD or equivalent, with recent clinical practice.
• Experience in quality improvement, clinical education, guideline development or research.
• Clear writing and comfort making judgement calls on ambiguous cases.
• Interest in how models reason about medicine and where they shouldn’t be trusted.

Helpful, not required

• Informatics experience or familiarity with EHR data.
• Prior work with peer review, examination boards or clinical annotation.