Senior Principal Scientist / Associate Director, Lead Scientific Data Governance

Job Description Summary

In this role, you will spearhead strategic data governance projects to maximize the value of our scientific data assets, ensuring they are structured, interoperable, and ready for advanced analytics and AI applications. Working closely with scientific teams and IT partners, you will translate complex scientific workflows into scalable, efficient data solutions. Success in this position requires strong communication and project management skills, broad expertise in drug discovery, and the ability to operate effectively within large, matrixed organizations. You will play a pivotal role in shaping how data is leveraged across scientific domains to accelerate drug discovery and enable enterprise-wide innovation.

Job Description

Internal Job Title : Sr. Principal Scientist, Data Science / Associate Director, Data Science

Position Location : onsite, Cambridge, MA

#LI-Onsite

Are you ready to unlock the power of scientific data assets and advance datadriven innovation in drug discovery? Join us at Novartis BioMedical Research as a member of the Discovery Sciences (DSc) Data Excellence team and help shape the future of scientific data governance.

In this role, you will spearhead strategic data governance projects to maximize the value of our scientific data assets, ensuring they are structured, interoperable, and ready for advanced analytics and AI applications. Working closely with scientific teams and IT partners, you will translate complex scientific workflows into scalable, efficient data solutions. Success in this position requires strong communication and project management skills, broad expertise in drug discovery, and the ability to operate effectively within large, matrixed organizations. You will play a pivotal role in shaping how data is leveraged across scientific domains to accelerate drug discovery and enable enterprise-wide innovation.

Key Responsibilities

Lead the execution of data governance projects across DSc scientific data domains.

Guide cross-functional teams to define and implement robust data standards, establish metadata requirements, and develop effective stewardship practices.

Ensure scientific data is captured with rich contextual metadata to enable reuse and interoperability across platforms.

Facilitate the harmonization of scientific workflows and data products by partnering with matrixed teams across the organization.

Serve as the business owner of key scientific data applications and work with IT product teams to ensure tailored implementation and support.

Promote data literacy and drive adoption of data governance frameworks.

Represent DSc on cross-functional data governance boards and contribute to the enterprise data strategy.

Essential Requirements

Advanced degree (Ph.D. or M.Sc.) in life sciences, data science, or a related field.

8+ years of experience in pharmaceutical R&D, with proven track record of working with diverse scientific data types (e.g., omics, imaging, screening).

Proven experience in data management and stewardship, with a focus on enabling harmonization and integration of diverse data assets across multiple systems.

Track record of successfully leading cross-functional initiatives in complex, matrixed organizations.

Excellent project management and stakeholder engagement skills.

Demonstrated ability to translate complex scientific workflows and technical requirements into scalable data solutions.

Demonstrated ability to communicate data strategy insights and recommendations to cross-functional stakeholders.

Desirable Requirements

Experience with database management systems and working knowledge of coding languages commonly used in scientific data workflows (e.g., SQL, Python, R).

Advanced expertise in FAIR principles, metadata standards, and enterprise data platforms

Specialized knowledge of data governance frameworks and data quality metrics within a drug discovery context.

The salary for this position is expected to range between : Sr. Principal Scientist, Data Science : $160,300 and $297,700 per year, and Associate Director, Data Science : $176,400 and $327,600 per year. The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors.

Your compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.

US-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days,

holidays and other leaves.

To learn more about the culture, rewards and benefits we offer our people click here.

EEO Statement :

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status.

Accessibility and reasonable accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to us.reasonableaccommodations@novartis.com or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.

Salary Range

$160,300.00 - $297,700.00

Skills Desired

Apache Spark, Artificial Intelligence (AI), Big Data, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Machine Learning (Ml), Master Data Management, Python (Programming Language), R (Programming Language), Statistical Analysis