Engineering Manager / Architect Data Science & ML

Technical Architecture & Hands-On Development

• Perform statistical analysis and apply advanced statistical modeling techniques


• Design, train, and optimize machine learning and deep learning models for various business use cases


• Deploy models into production environments and ensure reliable model serving and scalability


• Build and maintain robust GxP-compliant ML pipelines on cloud infrastructure (AWS/Azure/GCP) including data ingestion, preprocessing, training, and monitoring


• Evaluate model performance using appropriate metrics and continuously improve accuracy and efficiency


• Collaborate with cross-functional teams (engineering, product, and business stakeholders) to translate requirements into data-driven solutions


• Monitor deployed models for performance drift and retrain models as necessary


• Ensure best practices in model versioning, reproducibility, and documentation


• Conduct hands-on code reviews, architectural reviews, and model performance evaluations for all team deliverables.

People Leadership & Team Development

• Directly manage and mentor a team of 4–8 ML engineers, data scientists, and data engineers with defined technical goals and OKRs.


• Set individual development plans, run performance reviews, and build a culture of technical excellence, scientific rigor, and continuous learning.


• Recruit, interview, and hire top data science and ML engineering talent with strong domain experience in life sciences or regulated industries.


• Foster a collaborative environment where the team is empowered to propose, prototype, and ship novel ML solutions with speed and quality.


• Act as the technical escalation point for the team — unblocking architecture decisions, performance issues, and regulatory compliance questions.

Cross-Functional Collaboration

• Partner with the Product Manager (Data Science) to translate business and regulatory requirements into precise, implementable ML specifications.


• Collaborate with platform engineering on data infrastructure, API design, and integration of ML models into the ValGenesis SaaS platform.


• Work with the Quality and Compliance team to ensure all ML features are properly validated, documented, and audit-trail compliant under 21 CFR Part 11.


• Engage with strategic customers and key opinion leaders (KOLs) in the pharma industry to validate model approaches and gather technical feedback.


• Present technical architecture and model performance to executive leadership, customer CTOs, and regulatory affairs teams.

ML Operations & Governance

• Establish MLOps practices: model versioning, experiment tracking, automated retraining triggers, drift detection, and production monitoring.


• Define model governance standards for the GxP environment, including model qualification, change control, and periodic review procedures.


• Own the technical roadmap for the data science platform, balancing research innovation with production stability and regulatory compliance.

Education

• Degree in Statistics, Machine Learning, Computer Science, Biostatistics, Chemical Engineering, Pharmaceutical Sciences, or related quantitative field.


• Bachelor’s degree considered only with 8+ years of directly relevant hands-on experience.

Experience

• 5+ years of hands-on data science and ML engineering experience, with at least 3 years in a technical leadership or architect role.


• Proven track record of building and shipping production ML models at scale, not just research prototypes or POCs.


• 5+ years of experience in the pharmaceutical, biotech, or medical device industry in a technical role involving process data, quality analytics, or statistical modeling.


• Demonstrated hands-on experience building all model types listed in the Technical Skills Matrix above.


• Experience leading a team of 3+ data scientists or ML engineers with measurable outcomes.