Senior Director, Neuroscience Translational Analytics

Job Description

Position Description:

Senior Director, Neuroscience Translational Analytics

The Translational Genome Analytics group within the Data and Genome Sciences Department is recruiting a Senior Director to lead a Neuroscience Translational Analytics team, based in Cambridge, MA. We are seeking an experienced data scientist with deep expertise in discovering novel therapeutic targets and biomarkers for neurodegenerative diseases and other nervous system disorders of relevance to our expanding Neuroscience portfolio.

You will lead a growing team of data scientists to inform our Neuroscience drug development pipeline. As such, you will leverage advanced AI/ML approaches to integrate deep multi-modal patient profiles, human genetics and functional genomics data to decode causal disease mechanisms to discover novel therapeutic targets and precision biomarkers across our Neuroscience portfolio.

You will shape data science and AI/ML driven discovery strategies across our company's Neuroscience portfolio and serve as a strategic partner to scientists from across our Discovery, Preclinical, Translational Medicine and Global Clinical Development organizations. You will also assume a hands-on leadership role, implementing rigorous data analysis practices, aligning resources based on scientific priorities, and mentoring and developing talented members of the Neuroscience Translational Analytics team. 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:

• Adopt cutting-edge and scalable data science and bioinformatics approaches to integrate muti-resolution genetic, transcriptomic, proteomic, epigenomic and imaging data derived from patient tissues, cerebrospinal fluid, serum to decode disease biology, identify therapeutic targets and discover patient stratification biomarkers in complex diseases of the nervous system

• Deploy advanced AI/ML approaches to leverage spatial and single-cell multi-omics data derived from tissue samples spanning disease and healthy controls in order to decode disease-relevant cellular interactions and cell-type specific molecular networks with the goal of novel target and biomarker discovery in complex neurodegenerative diseases (e.g. AD, PD, ALS) and other nervous system disorders

• Integrate patient-derived data with high-throughput genetic and chemical screens in translationally-relevant preclinical models, including pooled/arrayed CRISPR, PerturbSeq, Optical Pooled Screens, Cell Painting and proteomic readouts to decode causal disease biology and prioritize novel therapeutic targets

• Mentor and hire outstanding computational biologists and data scientists responsible for analysis of a wide variety of patient-derived and preclinical data to advance our company's Neuroscience portfolio.

• Continually enhance our foundational Neuroscience data assets by building upon existing and expanding collaborations with academia and industry partners

• Collaborate with internal peers in diverse functional areas (Neuroscience, Information Technology, in vitro and in vivo pharmacology, chemistry, clinical and translational biomarkers) in a matrixed environment to develop research plans, optimize resource allocation, and maximize efficiencies to best execute on programs

• Clearly communicate results to project teams, our company's scientific community, and the external scientific community through internal documents, presentations, and publications in leading journals

Travel

• Up to 10% travel is required

Position Qualifications:

Education Minimum Requirement:

• Ph.D. plus a minimum of 7 years post-doctoral research experience

Required Experience and Skills:

• A solid track record of 4+ years building/leading high-performance computational and AI/ML teams and developing top data science talent in biopharma or related industry with a specific focus on complex neurodegenerative diseases

• Demonstrated expertise to decode disease biology by leveraging advanced machine learning and/or statistical inference frameworks to analyze multi-modal (transcriptomic, epigenomic) and multi-scale (bulk, single-cell and/or spatial) patient-derived data

• Experience with in applying state-of-the-art analytic approaches to leverage preclinical functional genomics approaches (e.g., pooled/arrayed CRISPR screens, PerturbSeq, etc) to decode causal disease biology

• Strong ability to code in R/Python and best practices for reproducible data analyses