Grupo Protege

Product Manager

• Own how customers access, evaluate, and receive data—from samples and evaluation sets to full contractual deliveries
• Define the product surfaces and mechanisms for data delivery
• Partner with various teams to enforce privacy & rights at Point of Access
• Identify and standardize delivery processes to reduce bespoke work
• Define self-serve access for customers under specific conditions
• Enable auditability and reproducibility of delivery processes

Product Manager, Data Access – Delivery

• Build our Internal Data Catalog
• Define the data model across title, asset, and partner levels.
• Establish a clear state model (e.g., ingested, accessible, in pipeline, linked/enriched).
• Own search and discovery across modalities; set metadata standards with Data Lab + Engineering.
• Ensure catalog state is auditable and resilient to refreshes, moves, and deletions.
• Create structured visibility into partner supply not yet ingested (potential, cadence, modality coverage, volume estimates).
• Enable GTM to scope deals based on *available + accessible* supply—not only what’s already in-platform.
• Provide visibility into partner inclusion in deals, utilization trends, and inventory footprint.
• Reduce partner back-and-forth caused by unclear system truth.
• Partner with Partnerships to ensure relationships are supported by scalable system representations
• Work closely with Privacy, Rights & Trust: represent data eligibility and constraints
• Work closely with Data Access & Delivery: to ensure discoverable supply is deliverable
• Work closely with Solutions Architecture: to identify catalog gaps surfaced by deals
• Work closely with Engineering: owns ingestion execution and infrastructure

Product Manager, Data Inventory, Partner Systems, Catalog

• Own Protege’s privacy protection strategy across modalities (text, audio, video, multimodal), balancing privacy, usability, and cost.
• Define scalable de-identification workflows, including approaches that preserve longitudinal or contextual signal when needed.
• Own rights, permissions, and usage constraints: define how they are represented in the platform and enforced end-to-end.
• Build validation mechanisms for partner-performed de-identification and rights claims to ensure reliability at scale.
• Shape product abstractions for regulated environments (e.g., HIPAA, GDPR, international healthcare) without hard-coding policy into one-off solutions.
• Identify and reduce trust/risk surfaces where inaccurate partner metadata, unclear provenance, or weak validation creates legal or reputational risk.
• Define product signals and guardrails that make risk visible and manageable.

Product Manager, Privacy, Rights & Trust

AI Research Scientist

• Conduct feasibility analyses by querying healthcare datasets to assess patient cohort availability based on complex inclusion/exclusion criteria (i.e. procedures, diagnoses, diversity, longitudinal completeness, regulatory constraints)
• Collaborate directly with customers to understand their use cases and support effective data integration
• Ensure customers have a clear understanding of the data’s structure, limitations, and strengths
• Identify gaps in our data offerings and provide insights to our partnerships team on the highest-priority data acquisitions
• Evaluate potential data partnerships, ensuring the data is high-quality, well-documented, and commercially viable

Applied ML Researcher

Data Scientist

• Design and apply statistical and machine learning methods to curate, filter, and enrich large-scale unstructured datasets
• Develop frameworks to assess data diversity, duplication, and informativeness. Design statistical approaches to de-risk training datasets
• Collaborate with model training teams to identify data bottlenecks and optimize dataset performance. Emphasis on ability to collaborate with large foundational models and smaller startups
• Provide leadership on data quality strategy and shape internal best practices
• Evaluate external datasets for integration, focusing on scalability, quality, and relevance to model performance. Help build data scorecards
• Contribute to research and development of tools that automate data preprocessing and validation

Senior Data Scientist

• Own end-to-end fulfillment for Media deals, from pre sales sampling through to final delivery
• Oversee execution across video, audio, and motion capture pipelines, ensuring consistency and quality
• Partner closely with Sales, Partnerships, Engineering, and Product to translate deal terms into executable plans
• Manage scope, timelines, resourcing, and risk across multiple concurrent deals
• Design, implement, and continuously improve operational workflows for the Media vertical
• Build scalable systems for scoping work, scheduling, production tracking, and delivery
• Establish clear success metrics, reporting, and accountability across projects
• Identify bottlenecks and proactively solve for scale, efficiency, and quality
• Manage and grow a media product success team for each vertical
• Set clear goals, roles, and expectations for team members
• Coach and develop high-performing operators and project leads
• Foster a culture of ownership, clarity, and execution excellence
• Serve as the primary point of coordination between Media, Product, Engineering, Sales, Product and external partners

Head of Product Success – Media

Research Scientist

Senior Applied Research Scientist

Growth Marketing

• Own end-to-end execution of Protege's conference and events strategy across multiple verticals and customer/partner/relationship profiles
• Build and maintain our events playbook: prioritize conferences and events, coordinate logistics, manage budgets, and ensure calendars are stuffed with prospect meetings
• Partner with commercial and partnerships teams to execute pre-event demand generation and maximize on-site meeting pipeline
• Coordinate event presence, speaking opportunities, side events, and dinners that position Protege in the market
• Assist with marketing operations infrastructure: CRM integration, lead attribution, automated workflows, and reporting that connects events to revenue
• Support growth marketing initiatives including social media content, sales enablement materials, and vertical-specific collateral

Growth Marketing Manager

• Partner closely with Data Lab scientists to understand how models are being trained, evaluated, and iterated today
• Translate experimental workflows (data curation, labeling, evaluation, fine-tuning, feedback loops) into scalable product and platform capabilities
• Identify patterns across experiments that are worth standardizing versus those that should remain bespoke
• Lead product discovery and execution for internal tools that support modern AI development:
• dataset versioning
• evaluation pipelines
• annotation and human-in-the-loop workflows
• experiment tracking and reproducibility
• Ensure tooling reflects real-world frontier practices, not academic abstractions
• Serve as the primary product interface for the Data Lab
• Translate research intuition into product requirements engineers can build against
• Help researchers reason about tradeoffs between novelty, robustness, and scalability
• Collaborate with Platform and Vertical PMs to ensure new capabilities integrate cleanly into customer-facing products
• Decide when an experimental capability is ready to move from “research mode” to “product mode”
• Apply an 80/20 mindset without undermining scientific rigor
• Sunset or deprioritize tools and ideas that do not meaningfully advance AI development velocity or data quality
• Define success metrics tied to:
• experiment cycle time
• researcher productivity
• adoption of internal tools
• downstream impact on customer data products
• Use qualitative and quantitative feedback to continuously iterate