Staff Data Scientist, Graph ML

Valo Health•Lexington, MA

About The Position

Valo Health is a technology company applying human and machine intelligence to accelerate the creation of life-changing medical treatments. At the core of this vision is Valo’s computational platform: an end-to-end, integrated drug discovery and development engine built from the ground up. Valo hires the best and gives them first-class training and support. We approach our work fearlessly, learn quickly, improve constantly, and celebrate our wins. A centerpiece of our culture is our commitment to inclusion across race, gender, age, religion, identity, and experience. Diversity fuels the Valo experience and drives us every day. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential. As Staff Data Scientist, Graph ML, you will develop and deploy graph ML solutions to synthesize and extract novel insights from Valo data in the context of a vast corpus of knowledge in medicine, molecular biology, human genetics, and drugs, to drive compute-enabled biological hypothesis generation. Our innovative platform leverages advanced computational techniques, starting with patient data, to bring better medicines to patients, faster. You will be responsible for developing and delivering graph ML and network biology-based analyses that generate and support drug target hypotheses on the path to discovery of new medicines. You will collaborate closely with a diverse group of data scientists and biologists to enable the contextualization of predicted drug targets within relevant patient subpopulations. You will also be accountable for communicating methodological approaches and key results to internal and external cross-functional stakeholders. Additionally, you will work with other data scientists, software engineers, and data engineers to continue building and improving Valo’s integrated graph platform to accelerate insights across multiple projects and applications. A successful candidate brings deep technical expertise in graph machine learning, network analytics, and modern data science best practices, along with experience in biology research in the context of drug discovery, and curiosity and excitement to learn.

Requirements

MS or PhD in a quantitative field with extensive experience at the intersection of machine learning and graph analytics
Experience in healthcare, medicine, molecular biology, computational biology, or life sciences.
Advanced knowledge of and experience with graph ML techniques such as Graph Neural Network (GNN) models applied to link prediction, node classification, and other biomedical-relevant computational tasks, as well as related explainability methods
Experience or general knowledge of knowledge-graph building and graph databases
Familiarity with general graph algorithms and relevant Python libraries
Strong experience in Python and machine learning and/or deep-learning frameworks (e.g., pytorch)
Experience with data science best practices (data provenance, code versioning, reproducibility, git, etc), large-scale data analytics engines (e.g., Spark or Dask), and working in cloud environments (e.g., AWS)
Strong data visualization, analytical, problem-solving, and communication skills, with demonstrated ability to condense, summarize, and synthesize results into informative and actionable presentations to experts from different fields.

Nice To Haves

Experience with traditional drug discovery and development processes and approaches
Domain expertise in neuroscience, immunology, and/or cardio-metabolic biology
Knowledge of related data science domains, including statistical genetics, multi-omics & real-world evidence

Responsibilities

Lead the design, implementation and application of graph ML and network biology approaches to target discovery from RWD and multi-omic datasets.
Prototype new approaches aimed at enhancing and improving Valo’s graph platform, seeking out new scientific opportunities to increase the team’s impact.
Work with world-class engineers to ensure that graph methodologies, graph construction, and underlying data are robustly integrated, to develop generalizable solutions to core scientific problems.
Apply your technical knowledge and intuition to break down large problems into solvable pieces. Time is limited; you’ll need to prioritize which problems are critical-path today from those that can wait.
Be an agile and pro-active Data Science team member, providing regular updates on your work, and input into the work of your colleagues; championing data science best practices; participating in code, design, and analysis review.