What if… you could join an organization that creates, resources, and builds life sciences companies that invent breakthrough technologies to transform human health and sustainability?

Company Summary:
Quotient Therapeutics, Inc. is a privately held, early-stage company developing a novel genomics platform and therapeutics for diseases with a large unmet need.

Quotient Therapeutics, Inc. was founded by Flagship Pioneering, an innovative enterprise that conceives, creates, resources, and grows first-in-category life sciences companies. Flagship Pioneering has created over 100 groundbreaking companies over the past twenty years, all of which are pioneering novel and proprietary biological, industrial, and engineering approaches to solve major needs in human health and sustainability. These companies include Moderna (MRNA) Generate Biomedicines, Sana Biotechnology (SANA), Tessera Therapeutics, Evelo Biosciences (EVLO), Indigo Agriculture, Seres Therapeutics (MCRB), and Syros Pharmaceuticals (SYRS). To date, Flagship has deployed over $2.5 billion in capital toward the founding and growth of its pioneering companies alongside more than $19 billion of follow-on investments from other institutions. In 2021, Flagship Pioneering was ranked 12th globally on Fortune’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies.

Position Summary:

We are seeking a visionary Senior AI/ML Scientist to be part of ground-breaking research at the forefront of somatic genomics. You will be part of a dynamic team at Quotient’s multidisciplinary research environment comprising AI scientists, computational biologists, genome scientists, software engineers and disease area specialists. You will also work closely with specialists in translational science, drug discovery and pre-clinical modelling. In this role you will be responsible for designing and implementing novel machine learning and deep learning methods applied to somatic genomics. This includes identifying research problems that could be addressed through structured or unstructured, complex omics and imaging data and developing appropriate models and analytical solutions.  

Responsibilities:

• Develop and implement novel machine learning and deep learning methods to address key challenges in somatic genomics, ranging from imaging data analysis to variant interpretation.
• Extract research and business value from complex, unstructured somatic genomics data and metadata.
• Optimize large-scale data preparation, enhance analytics platforms, and industrialize validated analytical methods in collaboration with the data engineering team.
• Uncover novel biological insights into disease, spanning complex and rare diseases and develop methods to validate new drug targets.
• Evaluate the scientific and technical integrity of algorithms and tools within the analysis pipeline.
• Work closely with discovery and development teams to integrate genomic and histology imaging analysis.
• Communicate findings effectively to both technical and non-technical audiences.

Key Qualifications:

• PhD (or equivalent experience) in Machine Learning, Computational Biology, Bioinformatics, or a related quantitative field.
• Strong programming skills with a solid understanding of algorithms and data structures.
• Proficiency in Python with hands-on experience using open-source ML frameworks such as scikit-learn, PyTorch, TensorFlow, or Keras.
• Extensive experience with Machine Learning and Deep Learning models (e.g., tree-based methods, CNNs, Transformers) and developing robust, production-ready ML systems.
• Strong communication skills, with the ability to convey complex ideas to both experts and non-experts through discussion and clear documentation.
• Excellent problem-solving and prioritization skills, with a collaborative mindset suited to a fast-paced, evolving environment.
• Passion for applying machine learning to life sciences, with an understanding of genomics and its impact on drug discovery.

Additional Preferred Skills 

• Experience with AI Vision techniques (e.g. image segmentation, classification).
• Familiarity with Generative AI (LLMs).
• Experience with large-scale multi-omics data analysis.
• Hands-on experience with cloud computing (AWS).