Staff Data Scientist (Graph ML) | Valo Health · Teeming.ai
Valo Health
Valo is a technology company focused on accelerating drug discovery and development through the integration of human-centric data and AI-powered computation. Their Opal Computational Platform™…
Valo is a technology company focused on accelerating drug discovery and development through the integration of human-centric data and AI-powered computation. Their Opal Computational Platform™…
Sector: Biotechnology Research — AI-driven drug discovery
Platform: Opal computational platform (AI, human data, predictive chemistry)
Disclosed funding: ≈ $460M (multiple rounds; Series B closed at $300M)
Company Overview
Problem Domain
Drug discovery and development acceleration using AI and human-centric data.
Founded
2019
Industry
Biotechnology Research
Tech Stack
Cloudflare
Google Analytics
Google Tag Manager
Funding Track Record
Series B- 2021-03-09
300000000
Series B closed at $300M after a $110M extension from Koch Disruptive Technologies (extension announced March 9, 2021).
- 2022-10-28
Most recently disclosed funding date in provided profile.
Investor Signal
“Founded and initially capitalized by Flagship Pioneering; later investors include Koch Disruptive Technologies and multiple strategic/financial backers listed across disclosures.”
Founders
What we do
Join the Team
Staff Data Scientist (Graph ML)
On-SiteLexington, Massachusetts, United States, US
On-Site • Lexington, Massachusetts, United States, US
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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
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
Benefits
Startup jobs. A lot of them.
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Medical, Dental, and Vision Insurance
Flexible Time-off
401K
Corporate Engagement Opportunities with Local Non-Profit Organizations
Commuter Reimbursement
Fitness Reimbursement
Pet Insurance- 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
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
MS or PhD in a quantitative field with extensive experience at the intersection of machine learning and graph analytics
Familiarity with general graph algorithms and relevant Python libraries
Experience or general knowledge of knowledge-graph building and graph databases
Strong experience in Python and machine learning and/or deep-learning frameworks (e.g., pytorch)
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
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)
Experience in healthcare, medicine, molecular biology, computational biology, or life sciences
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
