Engineering Manager / Architect — Data Science & ML

Responsibilities:

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.

Requirements:

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.

PREFERRED QUALIFICATIONS

Experience at a life sciences SaaS company or pharmaceutical analytics vendor

Contributions to open-source ML libraries, statistical packages, or pharmaceutical data standards (CDISC, SDTM, ADaM).

Certified in cloud ML platforms (AWS ML Specialty, Azure Data Scientist, GCP ML Engineer).

Six Sigma Black Belt or ASQ CQE with a strong statistical application background.