Associate Director, Safety Data Solutions Architect

Description

The company is a healthcare business working across Innovative Medicine and MedTech, and its Global Medical Safety group is hiring for this role. The job sits in the Data Analytics and Computational Sciences function, in the Data Science sub-function, under the Scientific/Technology category.

The group runs pharmacovigilance and safety analytics on Google Cloud Platform (GCP). Your job is to set the target architecture, standards and delivery plan for that work, and to turn safety science needs into validated, scalable and compliant data and AI capabilities.

You will work with Safety Analytics Data Scientists on LLM and GenAI use cases, setting the platform guardrails, retrieval and inference patterns and compliance controls. You will coordinate Principal-level data engineers and cross-functional teams to produce trusted, inspection-ready data products. Hands-on work is a small part of the role and centres on reference implementations and golden-path templates that help teams adopt the platform faster.

Job locations are Horsham, Pennsylvania and Titusville, New Jersey, United States of America.

Responsibilities

• Set baselines and measures for data onboarding speed, solution reuse, platform reliability, inspection readiness, cost efficiency and time to insight for safety scientists, and show the value each change delivers.
• Work with safety, product, data science and technology leaders to turn business and scientific workflows into a multi-year data and platform roadmap. Cover build-versus-buy calls, investment order, adoption measures and the retirement of legacy technology.
• Define governed lifecycle patterns and platform controls for AI-enabled solutions, covering evaluation, approval, versioning, deployment, monitoring and reproducibility. Include human oversight, provider portability, rollback and control over models, prompts, retrieval sources and agent tools. Keep platform governance separate from scientific ownership of model behaviour.
• Define curated, domain-aligned data products for pharmacovigilance, including signal detection, case analytics and predictive safety, with clear quality, reliability and inspection-readiness criteria.
• Own current and transitional architectures for ingestion, transformation, storage, serving and consumption on GCP, using tools such as BigQuery, Dataform and Cloud Storage. Encourage reuse through reference architectures and patterns.
• Set guardrails for AI, ML and GenAI integrations, including embeddings, vector stores, semantic retrieval and RAG, with focus on explainability, traceability and responsible AI. Work with the Safety Analytics Data Scientists without taking over model development.
• Set up canonical data models, data contracts and lifecycle practices. Use contract-first integration, schema versioning and controlled interface changes between producers and consumers.
• Chair design reviews, approve solution designs and enforce non-functional requirements for resilience, scalability, security, observability and SLOs. Clear delivery blockers and make sure quality and compliance hold.
• Lead data quality, governance and observability work. Cover validation standards, lineage and metadata, privacy and security controls such as least privilege, data minimisation and PII/PHI protection, and clear paths from R&D to production.
• Set standards for APIs, microservices and retrieval services on GCP, such as Cloud Run and GKE. Define reliability and security baselines, including SLO and SLA targets, error budgets, zero trust, secrets management and telemetry.
• Advance event-driven and streaming architectures using tools such as Pub/Sub and Dataflow, with contract-first integration, backward-compatible change and solid monitoring.
• Work with Safety Analytics Data Scientists and MLOps to define contract-first model and inference interfaces for batch and online use. Cover rate limits, cost controls, telemetry and audit logging. Data Scientists keep ownership of prompt design and model behaviour.
• Govern CI/CD for data pipelines and analytics and retrieval services. Build in policy-as-code, testing, artifact integrity and promotion gates. Own cost governance, including budgets, showback and chargeback, efficiency targets, right-sizing and usage policies.
• Set priorities and scope for Principal-level engineers across pipelines, services, feature stores and AI/ML integrations. Manage dependencies across teams, explain decisions to technical and non-technical audiences, and mentor people to raise standards.
• Hands-on, selectively: build and maintain reference implementations and design spikes, such as ingestion to BigQuery, contract-first schemas, Cloud Run service scaffolds and Pub/Sub or Dataflow patterns, to lower delivery risk and speed up adoption.

Requirements

• 10+ years in data or analytics engineering and architecture, including 3+ years in solution or enterprise architecture roles.
• Proven record leading Principal or Senior engineers and cross-functional teams in regulated environments.
• Deep architecture experience on GCP, including BigQuery, Dataform and Cloud Storage, plus orchestration and automation. Familiarity with Pub/Sub, Dataflow, and services on Cloud Run and/or GKE.
• Strong data modelling for analytics and ML, in batch and streaming, along with performance tuning and observability practice.
• Proven experience with data contracts, validation, schema versioning, lineage, metadata and access control.
• Practical understanding of AI, ML and GenAI patterns for analytics, such as feature engineering, vector stores, semantic retrieval and RAG, with focus on explainability and controls. You work alongside Data Scientists without owning model development.
• GxP, CSV or CSA experience for data and analytics platforms, with audit readiness and change control.
• Strong communication and stakeholder management. You can explain complex topics simply and influence senior leaders.
• Required skills: Advanced Analytics, Consulting, Critical Thinking, Data Analysis, Data Privacy Standards, Data Quality, Data Reporting, Data Savvy, Data Science, Data Visualization, Digital Fluency, Econometric Models, Mentorship, Strategic Thinking, Tactical Planning, Technical Credibility.
• Preferred: a Master's degree in Computer Science, Engineering or a related field, or equivalent experience.
• Preferred: experience in pharmaceuticals, life sciences, healthcare or another regulated domain.
• Preferred: experience with Terraform, CI/CD and policy-as-code.
• Preferred: exposure to feature store enablement and MLOps on GCP. Familiarity with Vertex AI is a plus.
• Preferred: Google Professional Cloud Architect or Professional Data Engineer, TOGAF or SAFe certification.
• Preferred: FinOps experience, including budget management and efficiency tracking for cloud analytics workloads.
• Preferred skills: the skills tagged for this role are listed under required skills above.

Conditions

This is a hybrid role, tagged LI-Hybrid.

The anticipated base pay range is $137,000.00 - $235,750.00.

Pay and benefits are subject to the terms of each plan and policy and to date of hire.

The company is committed to an interview process that accommodates applicants' needs, including disability accommodation requests.

Benefits

• Eligibility for the company's consolidated retirement plan (pension) and savings plan (401(k)).
• Eligibility for the company's long-term incentive program.
• Vacation: 120 hours per calendar year.
• Sick time: 40 hours per calendar year. Employees living in Colorado get 48 hours per calendar year, and those in Washington get 56 hours per calendar year.
• Holiday pay, including floating holidays: 13 days per calendar year.
• Work, personal and family time: up to 40 hours per calendar year.
• Parental leave: 480 hours within one year of the birth, adoption or foster care of a child.
• Bereavement leave: 240 hours for an immediate family member and 40 hours for an extended family member per calendar year.
• Caregiver leave: 80 hours in a 52-week rolling period, plus 10 days.
• Volunteer leave: 32 hours per calendar year.
• Military spouse time off: 80 hours per calendar year.
• More general information on company benefits is available from the company's careers site.