Senior Product Manager

Jobgether

11h•Hybrid

About The Position

In this role, you will lead the vision and evolution of an advanced analytics platform designed to transform complex healthcare and life sciences data into actionable insights. You will collaborate closely with machine learning engineers, data scientists, and research stakeholders to translate cutting-edge AI capabilities into intuitive, reliable product features. The position blends product strategy with hands-on experimentation, enabling rapid prototyping and data exploration to validate ideas quickly. You will help define quality benchmarks for AI-powered analytics while ensuring the platform delivers meaningful value to researchers and clinicians. This is an opportunity to operate at the intersection of product, data science, and healthcare innovation in a fast-paced, collaborative environment. The role requires both strong product instincts and the technical fluency to work directly with data and AI systems.

Requirements

3â5 years of product management experience working on highly technical, data-driven, or machine learningâpowered products.
Strong hands-on proficiency with SQL and Python for data exploration, analysis, and hypothesis validation.
Experience designing evaluation frameworks, benchmarks, or quality metrics for AI or ML systems and understanding how to define success for probabilistic outputs.
Proven ability to rapidly prototype and launch early-stage (0â1) product features, using experimentation and iteration to guide development.
Solid understanding of machine learning and AI concepts, enabling effective collaboration with data scientists and engineers.
Ability to operate in ambiguous environments and define success metrics for systems where outputs may vary across identical inputs.

Nice To Haves

Experience working with healthcare data, clinical analytics, or real-world evidence datasets (claims/EHR).
Familiarity with large-scale data processing frameworks such as PySpark or other distributed data systems.
Prior experience with AI evaluation pipelines, benchmarking methodologies, or production AI quality assurance.

Responsibilities

Partner closely with machine learning engineers and AI research teams to translate model capabilities into practical, user-facing analytics features that are reliable, interpretable, and impactful.
Rapidly prototype workflows and product features using AI-assisted development tools to validate concepts, test assumptions, and reduce engineering risk before full-scale development.
Define and lead benchmarking and evaluation strategies for AI-powered analytics, establishing quality standards and frameworks that ensure accuracy, reliability, and performance.
Work directly with large-scale healthcare datasets such as claims and EHR data using SQL and Python to inform product decisions and validate analytical outputs.
Develop multi-layered success metrics that connect model performance indicators (precision, recall, F1) with product adoption metrics and broader business outcomes.
Coordinate cross-functional collaboration across engineering, data science, design, and clinical or research stakeholders to ensure alignment and timely product delivery.