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Jobs(30,671)/Senior Data Scientist Role(369)/Flagship Pioneering, Inc. (9) - Senior Machine Learning Scientist
Flagship Pioneering, Inc.

Flagship Pioneering, Inc. - Senior Machine Learning Scientist

Cambridge, UK4w ago
In OfficeSeniorEMEAPharmaceuticalsBiotechnologySenior Data ScientistTalent AcquisitionPythonTraining Development

Requirements

• You do not need to meet every item to apply. Strong candidates may come from academia, industry, open-source work, or hands-on research. • PhD, Master’s degree, or equivalent experience in machine learning, computer science, computational biology, genomics, bioinformatics, statistics, engineering, or a related field. • Strong Python coding skills and experience building practical Deep Learning ML systems. • Experience using AI coding tools, coding assistants, LLM-based workflows, or agentic development tools. • Experience training, evaluating, or applying ML models to complex real-world data. • Depth in one or more of: biomedical foundation models, agentic AI, Perturb-seq, single-cell genomics, genotype–phenotype modeling, causal inference, perturbation modeling, or large-scale biological datasets. • Evidence of technical depth through papers, thesis work, open-source projects, industry projects, or substantial hands-on applied research. • Values and Behaviors: • Encourage respectful disagreement and cultivate open-minded, ego-free interactions to continuously push each other towards excellence • Seek out diverse perspectives; practice active listening and genuine curiosity to ensure all contributions are valued, regardless of source • Recognize the impact of your behavior, language and attitudes, and strive for balanced, meaningful exchanges that enhance mutual growth and understanding in all interactions • Use a company-first mindset to guide decision-making; prioritize team over individual success • Take calculated risks and challenge convention in the quest for exceptional outcomes • Quotient Therapeutics is a privately-held, early stage company developing breakthrough medicines informed by natural somatic genetic diversity present in patients. Through our work in somatic genomics, we are forging a new status quo for biopharma research and development across a broad pipeline of internal and partnered programs. • The company 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). • Quotient Therapeutics and Flagship Pioneering are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. • Recruitment & Staffing Agencies: • Quotient Therapeutics (“Quotient”) and Flagship Pioneering (“FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Quotient, FSP or their employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

Responsibilities

• Build ML models for genomic, single-cell, perturbation, and phenotype data. • Develop agentic AI workflows that help scientists analyze data, test hypotheses, and make better decisions. • Use AI coding tools to accelerate development while keeping code reliable, readable, tested, and reproducible. • Train and fine tune foundation models, language models, and representation learning methods for target discovery. • Model relationships between genotype, cell state, perturbation response, causal biology, and disease. • Collaborate closely with computational scientists, experimental biologists, software engineers, and target discovery teams. • Communicate results clearly, including model assumptions, limitations, and next steps.

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