Top Tech Jobs & Startup Jobs

Apply deep learning and generative AI to digital pathology and spatial biology datasets to develop image-analysis algorithms, biomarkers, segmentation, cell phenotyping, and spatial feature extraction. Collaborate with pathologists and translational teams, validate and benchmark methods, publish results, and present at scientific forums to support oncology drug discovery.

Owns a portfolio of NovaOS capabilities from concept through delivery, adoption, and continuous improvement. Responsibilities include managing product backlogs and roadmaps, prioritizing features, defining requirements and success metrics, coordinating cross-functional engineering, AI, UX, data, and architecture teams, leading product discovery and release planning, monitoring adoption and KPIs, and ensuring alignment with product governance, architecture, and Responsible AI standards.

Leads the strategy, roadmap, and product organization for NovaOS, an enterprise AI operating system. Oversees a portfolio spanning data, semantic, agentic, and experience layers; partners with business, engineering, and technology leaders to prioritize investments and deliver measurable outcomes. Establishes product governance, lifecycle practices, success metrics, and feedback mechanisms while building and mentoring a high-performing product management team.

Design and govern conceptual, logical, and physical enterprise data models supporting analytics, reporting, AI-ready datasets, and certified data products. Translate business requirements into scalable data structures across customer, product, payer, and territory domains. Establish modeling standards, review models against Common Data Model standards, maintain metadata and lineage accuracy, and resolve data gaps and inconsistencies. Collaborate with business, engineering, and technology stakeholders to provide modeling expertise and design guidance.

Leads enterprise reporting for data quality, governance, compliance, and operational performance. Designs executive dashboards, scorecards, and reporting packages; integrates metadata and quality metrics; aligns definitions and thresholds with stakeholders; oversees incident and remediation tracking; manages vendors, schedules, documentation, and change controls; validates data integrity; automates recurring reports; and escalates risks and compliance issues to leadership.

Lead enterprise data engineering standards, governed pipelines, data contracts, observability, quality controls, lineage, testing, and automation. Partner with governance, privacy, architecture, modeling, and platform teams to deliver reliable, compliant, AI-ready data products. Monitor operational performance and resolve pipeline and data reliability issues across complex enterprise environments.

Develop and execute a rare disease data strategy for the U.S. Commercial organization. Translate business priorities into data roadmaps, establish governance and metadata standards, coordinate data sourcing and vendor evaluations, manage delivery risks and dependencies, drive adoption of new ways of working, and report progress to senior stakeholders. Ensure data strategy activities align with privacy policies and regulations while optimizing data assets, products, and lifecycle management.

Leads the strategy, architecture, development, deployment, monitoring, and optimization of an agentic AI platform. Transforms products, processes, and workflows into scalable AI-enabled capabilities; establishes standards for performance, security, governance, and reliability; evaluates models and orchestration frameworks; partners cross-functionally on technical roadmaps; and builds and mentors high-performing engineering teams.

Owns day-to-day execution and evolution of NovaOS products, including backlog prioritization, requirements, user stories, releases, testing, and continuous improvement. Partners with business, engineering, UX, data science, AI, and architecture teams to deliver capabilities, monitor adoption and analytics, ensure governance and Responsible AI alignment, and drive user adoption.

Leads enterprise core data management by defining standards for data, metadata, modeling, and quality; driving governance adoption; and delivering trusted, AI-ready datasets. Oversees data operations, service levels, documentation, metrics, risk management, and continuous improvement. Partners with engineering, business teams, vendors, and senior stakeholders while leading and developing Associate Directors and distributed teams.