Principal Technical Product Manager, Data Platform

SandboxAQ's AI Simulation (AISim) division develops and applies Large Quantitative Models (LQMs) that are grounded in physics, chemistry, mathematics and biology to simulate molecular behavior with scientific precision and accelerate discovery in the physical world across biopharma, chemicals, advanced materials, healthcare, and so much more. The AISim division is looking for a seasoned Product Manager to join this team to build and own the Data Platform that underpins AISim's scientific discovery platform spanning Drug Discovery (BioSim) and Materials Design (ChemSim) applications. This is a senior individual contributor role: you'll lead through product craft and cross-functional influence, partnering closely with engineering, science, design, and trust/compliance.

Responsibilities

Define and own the Data Platform roadmap across ingestion, auto-detection, schema reconciliation, and lineage; Define and own the Knowledge Graph strategy and execution within AISim; Own the CRO and lab-partner ingestion-template product and the auto-matching UX, so partner data is usable the moment it lands; Drive dataset-level provenance, sensitivity tagging, and field-of-use governance; Design experiment-integration API schemas in partnership with the BioSim and ChemSim teams; Own the federated data-management product that enables IP-safe data sharing across partners; Define and track the metrics that matter including time-to-usable per dataset, auto-match accuracy, and lineage completeness

Requirements

5+ years in product management, with depth in data platforms or data governance; Hands-on familiarity with ETL/ELT pipelines, data catalogs, and provenance / lineage tracking; Track record designing developer-facing data APIs and ingestion tooling; Experience defining data governance controls, including access, sensitivity classification, and purpose / field-of-use restrictions; Ability to turn ambiguous, cross-team data problems into scalable, measurable products; Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

Nice to have

Experience with scientific data formats (HDF5, SDF, CSV, JSON-LD); Understanding of federated learning and IP-sharing constraints in R&D contexts; Ability to design experiences that are consumable both by research scientists and agents; Knowledge of regulated data governance (HIPAA, FedRAMP, GDPR, SOC 2, GxP / 21 CFR Part 11); Experience supporting audit, evidence-export, or compliance-attestation workflows; Familiarity with the drug-discovery or materials-research data lifecycle; MBA or equivalent business experience

Benefits

Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions; Retirement savings with company matching; Paid parental leave; Inclusive family-building benefits; Competitive base salary; Performance-based incentives or bonuses (where applicable); Equity participation; Flexible paid time off; Company-wide seasonal breaks; Support for flexible work arrangements; Opportunities for continuous learning and growth through on-the-job development and internal learning and development programs