Principal Technical Product Manager, Data Platform

Role Description

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 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 includes the end-to-end data management lifecycle:

• How data is ingested from CROs and lab partners
• Automatically detected and reconciled
• Tracked end-to-end for lineage and provenance
• Governed by sensitivity and field-of-use controls

Your charter spans the full journey of a dataset; from the moment it lands to the moment it is safely used in a model or exported as certifiable evidence.

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. We’re customer-obsessed in practice and all platform decisions trace back to real internal and partner needs. We value collaborative problem solving, ownership, transparent communication, and humility.

Qualifications

• 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 — 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.

Requirements

• Experience with scientific data formats (HDF5, SDF, CSV, JSON-LD).
• Understanding of federated learning and IP-sharing constraints in R&D contexts.
• 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 and inclusive family-building benefits.
• Fully remote work and flexible paid time off.
• Company-wide seasonal breaks and support for flexible work arrangements.
• Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.