Pioneering Medicines: Principal Scientist Bioinformatics

Flagship Pioneering, Inc.•Cambridge, MA

About The Position

Pioneering Medicines, a division of Flagship Pioneering, is building a world-class biopharmaceutical R&D capability focused on conceiving and developing life-changing treatments for patients by harnessing the power of Flagship's scientific platforms and applying those innovative approaches to serious diseases with unmet medical need. Unique to Pioneering Medicines’ approach is the opportunity to combine platforms to create truly novel and potentially transformative treatments. About Flagship Pioneering: Flagship Pioneering conceives, creates, resources, and develops first-in-category life science platform companies to transform human health and sustainability. Since its founding in 2000, the firm has originated and fostered the development of more than 100 scientific ventures, resulting in over $34 billion in aggregate value, 500+ issued patents, and more than 50 clinical trials for novel therapeutic agents. Position Summary: We are seeking a highly capable bioinformatics expert to join the Pioneering Medicines Translational Sciences Team. This person will lead the computational efforts on drug discovery programs, integrate, analyze and interpret omics data and effectively communicate results to impact target discovery, translational and patient stratification efforts across disease areas. This individual will collaborate with the translational research, biology, ADME/Tox and early development teams, in order to help execute on research program strategy. This role is well-suited for individuals who combine scientific depth with strong computational acumen and thrive in dynamic, cross-functional environments.

Requirements

Ph.D in Bioinformatics, Computational Biology, Biological Sciences, or related discipline and 4+ years of industry experience as a bioinformatics representative on drug discovery programs.
Understanding of biology and/or translational research and ability to address biological questions using computational approaches; in-depth knowledge of cancer genomics, tumor immunology, immunology or other related areas.
Succesful track record collaborating with cross-functional scientific teams.
Experience with state-of-the-art methods for next generation sequencing data.
Experience integrating genetic, genomic and pathway/network datasets with novel experimental data to derive biological understanding.
Fluency in R, Python or any other broadly used programming languages and environments like Code Ocean, AWS, git.
Demonstrated application of AI/ML to biological datasets, including algorithm development or use of libraries like scikit-learn, XGBoost, Pinnacle.
Excellent problem-solving, teamwork and communication skills, with an ability to effectively interact with people in a dynamic matrix environment, both internally and externally.

Nice To Haves

Exposure to translational science, patient stratification, or biomarker discovery in preclinical or clinical contexts.
Experience integrating heterogeneous omics data.
Knowledge of experimental design and data QC best practices.
Prior experience in biotech or pharma.

Responsibilities

Lead Translational Bioinformatics projects in various areas, including but not limited to oncology/I&I, cardiovascular, renal, metabolic and pulmonary diseases.
Coach other team members on strategic thinking and next level synthesis and communication skills.
Propose, evaluate and champion state-of-the-art computational omics approaches to answer key biological questions.
Mine genomics datasets to identify novel targets for small molecule and biological therapies.
Apply machine learning and AI approaches to uncover novel insights, including dimensionality reduction, clustering, classification, predictive modeling, and network-based inference.
Drive analyses or internalize expertise if needed, for a rich suite of data modalities: bulk and single cell RNAseq/genomics, spatial genomics, proteomics, and state-of-the-art modeling techniques, tailored to specific programs and disease areas.
Work closely with the in-vivo, in-vitro and translational research teams to translate biological questions into analytical solutions, including influencing experimental design, analyzing data and visualizing results.