Associate Director in Data Science, Digital Endpoints Pharma R & D (Billerica MA)

Merck•Billerica, MA

52d•Onsite

About The Position

In this pivotal role, you will build and establish scientific excellence in the development of digital biomarkers and endpoints within the Advanced Data Science team in the Data Sciences organization. You will lead the identification, development, and deployment of novel digital endpoints and outcome measures across clinical trials and life cycle management programs. Collaborating closely with the Integrated Health & Medicines organization, you will spearhead the design and validation of machine learning-driven algorithms and data processing pipelines that extract clinically relevant insights from high-volume digital health data sources, such as wearables, sensors and apps, mobile devices, and patient-facing applications. You will develop and implement advanced machine learning and data science methods to process multimodal time-series data generated by digital devices, ensuring scientific rigor, clinical validity, and regulatory readiness. As a subject matter expert, you will embed digital health technologies within evidence generation strategies, driving their acceptance in clinical trials and practice. You will also contribute to key regulatory filings and publications, advocating for the adoption of digital endpoints within the global R&D community. Staying at the forefront of emerging trends, you will explore and integrate emerging AI paradigms, including large language models (LLMs) and agentic AI systems, to support data interpretation, workflow automation, and digital health analytics. Your leadership will champion transformation efforts that unlock the full potential of Digital Health Technologies.

Requirements

PhD in a quantitative discipline (e.g., data science, computer science, biomedical engineering, statistics, physics, or related field) with a focus on large-scale data analysis.
Minimum of 7 years of relevant experience in the pharmaceutical, digital endpoints, AI/ML or related industry.
Proven track record in designing and validating digital endpoints or biomarkers for clinical applications.
Hands-on experience processing and analyzing large-scale data streams from digital health technologies, including wearables, sensors, smartphones, or connected medical devices.
Experience in building machine learning pipelines for time-series and multimodal sensor data, including feature extraction, signal processing, and model development.
Strong expertise in digital biomarker/endpoint development, including psychometric and statistical validation and regulatory aspects.
Solid understanding of modern machine learning and AI methods (e.g., deep learning, representation learning, time-series modeling) as applied to digital health and behavioral data.
Experience with scalable data processing and ML frameworks (e.g., Python data ecosystem, distributed data processing, or cloud/HPC environments).
Familiarity with generative AI and large language models, and an interest in emerging paradigms such as agentic LLM systems or AI-assisted scientific workflows.

Nice To Haves

Excellent stakeholder management skills and can navigate matrixed organizations effectively.
Strong communication and scientific writing skills, enabling you to translate complex technical content for diverse audiences.

Responsibilities

Lead the identification, development, and deployment of novel digital endpoints and outcome measures across clinical trials and life cycle management programs.
Spearhead the design and validation of machine learning-driven algorithms and data processing pipelines that extract clinically relevant insights from high-volume digital health data sources.
Develop and implement advanced machine learning and data science methods to process multimodal time-series data generated by digital devices, ensuring scientific rigor, clinical validity, and regulatory readiness.
Embed digital health technologies within evidence generation strategies, driving their acceptance in clinical trials and practice.
Contribute to key regulatory filings and publications, advocating for the adoption of digital endpoints within the global R&D community.
Explore and integrate emerging AI paradigms, including large language models (LLMs) and agentic AI systems, to support data interpretation, workflow automation, and digital health analytics.
Champion transformation efforts that unlock the full potential of Digital Health Technologies.