LIFE SCIENCES & BIOTECH
Clinical Trials · AI/ML & Biostatistics · Genomics/Multi-Omics · Evidence & Lineage
End-to-end ownership of product vision, roadmap, operating model, and lifecycle execution for AI-first life sciences platforms, with accountability for scientific validity, adoption, auditability, and production readiness across regulated research and clinical development environments.
FLAGSHIP PRODUCTS & SYSTEMS
Trial execution + evidence operating system (CTMS / EDC / eTMF–adjacent, platform-grade):
Built end-to-end trial workflow products spanning protocol → site activation → enrollment → visits → data cleaning → analysis → submission, with role-based tooling for Clinical Ops, Data Management, Biostats, Medical Monitor, QA, and Safety. Delivered true operational parity UX (deviations, queries, adjudication, monitoring findings, issue-to-resolution) so the product reflects how trials actually run, not slideware process models.
Decision intelligence & evidence continuity layer (AI/ML + lineage-first UX):
Productized hypothesis generation, cohort intelligence, and evidence navigation surfaces that convert heterogeneous research signals into decision-grade artifacts: candidate ranking, evidence cards, feature attribution, confidence UX, and “why this cohort / endpoint” narratives. Supported feasibility, enrichment, endpoint sensitivity, and operational risk prediction without obscuring uncertainty or provenance.
Biostatistics & analytics workbench (SAP → TFL → interim → CSR readiness):
Delivered biostatistics-facing products for analysis planning and execution, including structured SAP inputs, estimand mapping, randomization/stratification configuration, interim readiness, and TFL automation. Integrated reproducible analysis patterns (R / Python, controlled packages, versioned datasets) so results can be regenerated deterministically for review, audit, and submission.
Genomics & multi-omics evidence pipelines (research-to-clinical translation)
Designed product experiences and workflow primitives to operationalize genomics, transcriptomics, proteomics, metabolomics, and single-cell data across ingestion → QC → harmonization → feature generation → model training → interpretation. Supported domain formats (FASTQ/BAM/CRAM, VCF, AnnData/HDF5, sample manifests) with traceable transformations, batch-effect visibility, and data-fitness indicators.
Real-world evidence augmentation (when applicable):
Created evidence integration surfaces linking trial data with real-world signals (claims, EHR registries, outcomes, adherence proxies) to support external validity reasoning and post-market evidence planning, while enforcing governance, consent, and allowed-use constraints.
ENGINEERING & GOVERNANCE
GxP-grade “requirements → evidence” delivery mechanics (not documentation theater): Implemented evidence-forward delivery where audit artifacts are produced by normal work: PRD/user story → risk assessment → traceability → verification/validation-ready evidence aligned to GxP expectations. Embedded controlled changes, versioning, and release readiness checkpoints so regulated delivery remains fast and defensible.
Clinical trial compliance controls expressed as system behavior: Operationalized controls aligned to expectations such as ICH GCP, 21 CFR Part 11 (as applicable), and internal CSV/CSA practices—through role-based access (RBAC), e-signature–appropriate workflows, immutable audit trails for high-stakes actions, and controlled configuration for protocol changes, forms, and derived variables.
Standards-aware data interoperability (so analysis isn’t bespoke every time): Defined canonical data contracts and transformation rules for trial and biomarker data. Supporting standards and conventions such as CDISC (SDTM/ADaM/CDASH where applicable), controlled terminology, and consistent visit/epoch semantics. Enforced deterministic derivations (e.g., analysis-ready datasets), reproducible pipelines, and dataset versioning so “numbers don’t drift” across reruns.
Data integrity & lineage instrumentation as a first-class product capability: Built completeness/latency coverage dashboards, discrepancy detection, and exception funnels (missingness, outliers, inconsistent coding, visit window violations) so data quality is measurable and operational, not a retrospective scramble. Implemented provenance metadata (source, transformation step, pipeline version, parameter sets) enabling full traceability from raw → curated → feature → model → output.
AI governance that survives audit + production reality: Defined model acceptance criteria (calibration, bias checks where relevant, drift signals, latency thresholds), monitoring hooks, and incident playbooks; for GenAI-assisted workflows, enforced prompt/version controls, evaluation harnesses, guarded outputs, and human review loops appropriate for high-stakes scientific decisioning. Kept interpretability UX (attribution, evidence links, confidence) as a product requirement.
Enterprise architecture enablement (platform primitives > one-off scripts): Established integration patterns (API contracts, event schemas, dataset interfaces), canonical workflow states, and governance checkpoints tied to release readiness. With the goal that multiple teams (ops, data, stats, ML) can build on stable primitives without reinventing core trial and evidence logic.
PRODUCT MANAGEMENT & ENABLEMENT
0→1 incubation (science-to-product translation):
Ran structured discovery with SMEs across Clinical Ops, Data Mgmt, Biostats, Translational Science, and QA to convert ambiguous research asks into shippable increments: workflow maps, service blueprints, prototype validation, PRDs with measurable success criteria, and capability roadmaps grounded in real trial constraints (timelines, monitoring cadence, protocol amendments).
1→n scaling (sites/teams/programs) with readiness gates:
Scaled across studies and programs via readiness gates, SOP-aligned enablement, training/comms, support models, and adoption instrumentation. Standardized “study onboarding kits” (configuration templates, data mappings, QC checklists, playbooks for query management / interim readiness) so new trials don’t restart from zero.
Operating model + portfolio governance (so velocity doesn’t break compliance):
Implemented intake/prioritization, decision forums, KPI trees, and ProductOps rhythms tying roadmap sequencing to measurable outcomes: time-to-clean, query cycle time, data lock readiness, analysis reproducibility, model iteration speed, and audit readiness. Maintained crisp RACI boundaries across Product, Data, Stats, ML, and QA so ownership is explicit during deviations, incidents, and change control.
Cross-functional execution at platform scale:
Orchestrated product/UX/research/engineering/QA through architecture review rituals, release gating, and change management. Keeping scientific fidelity high while maintaining delivery throughput across multiple concurrent workstreams.
Long-term roadmap and portfolio strategy:
Defined and owned a multi-year product and platform roadmap spanning trial execution, evidence continuity, analytics, and AI enablement. Sequenced near-term scientific and operational wins with long-term capability evolution across interoperability, lineage, automation, and scalability. Thus, ensuring platforms mature from study-specific solutions into durable, reusable systems that support portfolio growth, regulatory readiness, and sustained velocity across programs.
OUTCOMES:
Trial execution velocity: Protocol-to-pilot ↓ 30–50% · site onboarding ↓ 25–40% · query resolution ↓ 20–35% · earlier interim-ready states with fewer late-stage surprises
Evidence integrity & audit readiness: >95% traceability coverage across critical artifacts · fewer analysis-drift incidents · faster audit response through lineage-first retrieval
AI/ML decision quality: 25–40% faster model iteration loops · improved signal-to-noise in hypothesis ranking via provenance UX and confidence gating
Operational efficiency: Manual reconciliation and rework ↓ 15–30% · clearer ownership reduced handoff loss across Ops ↔ DM ↔ Stats ↔ QA