Staff Data Scientist, Graph ML

About Us

Valo Health is a human-centric AI-enabled biotechnology company working to make new drugs for patients faster. The companys Opal Computational Platform transforms drug discovery and development through a unique combination ofreal-world data AI human translational models and predictive chemistry.

Our talented team of biologists chemists and engineers armed with advanced AI/ML tools work together to break down traditional R&D silos and accelerate the speed and scale of drug discovery and development.

Valo is committed to hiring diverse talent prioritizing growth and development fostering an inclusive environment and creating opportunities to bring together a group of different experiences backgrounds and voices to work together. We embrace new ways of learning solve complex problems and welcome diverse perspectives that can help us advance patient-centric innovation.

Valo is headquartered in Lexington MA with additional offices in New York NY and Tel Aviv Israel. To learn more visit.

Staff Data Scientist Graph ML

About Us 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 Valos 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 Valos 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.

What Youll Do

• 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 Valos graph platform seeking out new scientific opportunities to increase the teams 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; youll 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.

What You Bring

• 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 have

• 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

Compensation for the role will depend on a number of factors including a candidates qualifications skills competencies and experience. Valo Health currently offers healthcare coverage annual incentive program retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Valo Healths good faith estimate as of the date of publication and may be modified in the future.

Please note: At this time we are only able to consider candidates whocurrentlyhave permanent US work authorization without the need for immediate or future sponsorship.