Staff Data Scientist, Machine Learning

Key Skills

Java Programming, Python Programming, XML-JSON, Machine learning techniques, Jupyter Notebook, NoSQL, Kanban, R Programming, Statistical modeling

Job Description

Valo Health (formerly Integral 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 that is being built from the ground up.

Valo hires the best and gives them first-class training and support. If you’re driven to perform, you’ll fit right in! 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.

About the Role

Valo?is looking for an experienced?Staff Data Scientist to design and implement solutions to real-world small-molecule modeling problems to advance computationally accelerated drug development programs. You will apply and enhance the capabilities of Valo’s Opal platform to advance real-world, active drug development programs. Successful candidates must be committed to working with a diverse set of scientists, entrepreneurs, and domain experts in ways that cut across traditional industry boundaries in a fast-paced startup environment

Opal is an AI-based platform that leverages human-centric data to enable researchers to discover and develop new drugs. Opal is a fully integrated, AI-powered, cloud-native platform that leverages human-centric data to create new approaches to drug discovery and development enabling researchers to minimize the cost and time associated with discovery, development, and delivery of novel therapeutics. The predictive insights produced by Opal rely on high-quality, high-density human-centric data that is sourced from multiple data sets, processed both remotely and on site through a highly complex process.

Successful candidates will exhibit the following leadership traits:

• Customer Obsession: Mission- and vision-oriented product evangelist. Comfortable with being the face and voice of the product and mission in the market and across the company. Builder mentality. Sees ambiguity as opportunity, and obstacles as chances to build.
• Think Big: Innovative and creative, with a vision that transcends what is visible today.
• Earn Trust: Ability to build credibility and rapport with the executive team to drive collaboration and coordination with key stakeholders. High Emotional Quotient (EQ) with strong communication and influencing skills, to create corporate-wide alignment around product vision.
• Dealing with ambiguity: Comfortable with charting new territories and navigating with imperfect information and considering decisions of trade-offs.
• Bias for Action: High energy, low ego, and focus on finding data-driven solutions.

What you’ll do…

• Work with data scientists, researchers, product teams, and other domain experts to build solutions to complex data-oriented problems
• Train, assess, deploy, and interpret statistical machine learning models that inform and advance our programs.
• Design, develop, and scale data curation and modeling pipelines for our large-scale, high-throughput applications.

What you Bring…

• A PhD (or Masters with experience)?in Computational Chemistry, Computational Biology,? Bioinformatics, or related quantitative fields.
• Direct experience or exposure to the drug discovery process, with an understanding of how computational methods deliver impact on preclinical discovery (target identification & validation; small molecule development; elucidating molecular mechanisms of action and modifiers of effects)
• Experience with modern statistical analysis and machine learning techniques, particularly in the context of large-scale, high-throughput dataset analysis.
• Strong familiarity with the various tools and environments for successful, modern data science: beyond a fundamental comfort zone with the shell, Python/R, and other scripting languages, you have experience using HPC/grid/cloud computing environments, programming against API services, accessing data from a heterogeneous mixture of flatfiles, SQL relational databases, noSQL/JSON object storage, and/or RDF/OWL triple-stores.
• Strong analytical, problem-solving, and communication skills, including facility with Rmarkdown and/or Jupyter Notebooks for communicating reproducible results; and the ability to also condense, summarize, and synthesize those results into informative and actionable presentations to less technical audiences.
• Strong personal project management skills with significant practical experience managing your time split between multiple, parallel projects; experience with Agile processes and frameworks for team collaboration (e.g. Kanban, Atlassian tools)?