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Staff Data Scientist, Graph ML

SpringCube

Full time - Senior Engineer

Healthcare Services & Tech

United States, Boston - Massachusetts

Published 3 weeks ago

Salary: Disclosed upon interview

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Job Description

The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.

Company Overview
A human-centric, AI-enabled biotechnology company is focused on accelerating drug discovery and development through its proprietary computational platform. The organization integrates real-world data, AI, human translational models, and predictive chemistry to generate novel insights and support the discovery of life-changing medical treatments. With offices in Lexington, MA, New York, NY, and Tel Aviv, Israel, the company emphasizes inclusive growth, collaboration, and innovation across diverse teams of biologists, chemists, and engineers.

Summary
The Staff Data Scientist, Graph ML, will design, develop, and deploy graph machine learning (ML) solutions to extract actionable insights from complex biomedical data. The role involves generating biological hypotheses, supporting drug target discovery, and collaborating with cross-functional teams to integrate advanced computational techniques into the company’s graph platform.

Responsibilities

  • Lead the design, implementation, and application of graph ML and network biology approaches to drug target discovery from real-world and multi-omic datasets
  • Prototype and optimize novel approaches to enhance the company’s integrated graph platform
  • Collaborate with engineers to ensure robust graph methodologies, data integration, and generalizable solutions to scientific problems
  • Break down complex problems into manageable tasks and prioritize critical-path challenges
  • Provide regular updates, contribute to peer work, and champion data science best practices, including code and analysis review
  • Develop and deliver graph ML analyses to support drug target hypotheses
  • Communicate methodological approaches and key results to internal and external stakeholders

Qualifications

  • MS or PhD in a quantitative field with extensive experience in machine learning and graph analytics
  • Experience in healthcare, medicine, molecular biology, computational biology, or life sciences
  • Advanced knowledge of graph ML techniques, including Graph Neural Networks for link prediction, node classification, and explainability
  • Experience or understanding of knowledge-graph building and graph databases
  • Familiarity with general graph algorithms and Python libraries for graph analytics
  • Strong Python programming skills and experience with ML/deep-learning frameworks (e.g., PyTorch)
  • Knowledge of data science best practices (data provenance, version control, reproducibility, git)
  • Experience with large-scale data analytics (e.g., Spark or Dask) and cloud platforms (e.g., AWS)
  • Strong analytical, problem-solving, data visualization, and communication skills
  • Nice to have: experience in traditional drug discovery, neuroscience, immunology, cardio-metabolic biology, statistical genetics, multi-omics, or real-world evidence

Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals in globally.

1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
2. No Client Relationship: This company is not a client of SpringCube unless stated.
3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
4. No Liability: SpringCube is not liable for inaccuracies.

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