Sr. Director Clinicogenomics Machine Learning

MSD•Cambridge, MA

5d•Hybrid

About The Position

The Precision Genetics group within the Data, AI and Genome Sciences (DAGS) Department is recruiting a Sr. Director of Clinicogenomics Machine Learning. We are seeking an experienced machine learning scientist to lead our Data Science and Engineering efforts, specifically focused on leveraging patient data to fuel our discovery efforts. In this role, you will bridge the gap between clinical data and the earliest stages of discovery, shaping how data is acquired, integrated, and leveraged to accelerate decision-making and hypothesis-building. As a visionary leader, you will partner with executive leadership, subject matter experts in medicine and biology, and IT to scale advanced analytics, develop the use of AI, deliver solutions. While the team will initially be lean, we maintain a high bar for technical excellence. You will be responsible for ensuring that every model, pipeline, and analysis represents the cutting edge of Data Science and AI. You will serve as a strategic partner to our discovery colleagues across therapeutic area. You will also assume a hands-on leadership role, implementing rigorous data analysis practices, aligning resources based on scientific priorities, and mentoring and developing team members. You will operate in a highly collaborative environment, partnering with peers across multiple locations and in the broader scientific community. In this exciting role, you will:

Requirements

Ph.D. plus a minimum of 10 years of combined research experience in academia/pharma
A solid track record of 8+ years leading high-performance computational teams and developing top data science talent
Hands-on experience in interrogating patient clinical data to automate phenotyping
Ability to code in R/Python and establish best practices for reproducible data analyses
Strong ability to work and influence in complex matrixed organizations
Excellent oral and written communication skills
Ability to establish and maintain productive cross-site and high caliber external collaborations

Nice To Haves

Experience is establishing academic and consortia partnerships
Familiarity with defining treatment response for immunology, cardiometabolic and neuroscience indications from patient data
Ability to establish and maintain productive cross-site and high caliber external collaborations

Responsibilities

Set the Standard: Establish and maintain rigorous standards for code quality, model validation, and engineering best practices within the team
Cutting-Edge AI: Implement state-of-the-art machine learning architectures to extract signals from complex multi-modal patient data (e.g., omics, longitudinal claims data,etc).
Bespoke Modeling: Personally oversee and contribute to high-impact, custom modeling work for priority discovery programs.
Data Product Architecture: Design and deliver scalable data products that automate the flow from raw clinical data to discovery insights.
Build the Core: Recruit and mentor a lean, high-performing team of "full-stack" data scientists and engineers who can punch above their weight class.
Hands-on Visionary: Balance long-term strategic planning with the immediate need to be "hands-on" in the data and architecture during the initial growth phase.
Interoperability: Ensure all technical solutions are designed for seamless integration with IT partners and discovery-embedded data science teams.
Streamline Discovery: Partner with IT to automate data workflows, reducing the "time to insight" for discovery scientists.
Data Acquisition: Lead the strategy for identifying, evaluating, and acquiring high-value external patient datasets.