GeneDx is focused on delivering personalized, actionable insights that improve health outcomes. We sit at the intersection of diagnostics and data science, pairing decades of genomic expertise with an unmatched ability to interpret clinical data at scale.

Our exome and genome testing is among the best in the industry. We expect that it will be even more advanced in the future with the help of Centrellis®, our innovative health information platform. Powered by millions of medical records, Centrellis® integrates digital tools with artificial intelligence to ingest and synthesize clinical and genomic data.

As a result of our robust test menu, including our exome and genome testing, and the comprehensive insights generated by Centrellis®, we are developing a more complete understanding of complex disease than ever before. This translates to faster diagnosis, more effective treatment plans, and enhanced drug discovery.

Our offerings help a whole spectrum of healthcare partners -- clinicians, researchers, health systems, pharmaceutical companies, and payors -- improve patient experiences and advance population health.

In this role, you will be supported by the Head of Product Data Science. You will work in a team of 4-6 skilled analysts and data scientists. You will work with product owners, engineers, operations, and commercial leads as well as our users and customers to deeply understand what drives value in our products. You will help your colleagues understand what key outcomes will drive that value and how to measure it, as well as guide our future development efforts by modeling the return on investment of our work.

In this role, you will use your analytical skills to identify details of business processes and key metrics for us to track as our product and engineering teams work to enable our users with better tools. You will balance rigorous analysis with simplicity and urgency to make sure our product teams are making clear data-driven decisions as quickly as possible.

As a senior team member, you will be expected to clarify ambiguity quickly and accurately with non-technical stakeholders, breaking complex problems into small ones. You are expected to lead by demonstrating strong methods and practices, as well as contributing to the strategy and tactics of our team. While you are not expected to be an expert in machine learning, you will have a strong knowledge of statistical methods, and you will have the opportunity to develop machine learning skills as well as operations modeling, and other advanced statistical methods. This role will afford ample opportunity to deeply understand our patients, clinicians and industry partner user personas.
The Responsibilities

• Work with technical and non-technical team members in product & technology, operations and finance to use data to identify opportunities in lab operations.
• Dissect complex lab operations processes, identify and define precise metrics with lab operations product leads to measure delivered outcomes and align them with company OKRs.
• Use real world data from lab operations to build statistical models that identify potential opportunities for cost savings or efficiency improvements.
• Coordinate with our Dry Lab Operations and Data Warehouse teams as required to ensure key data are available for analyses and production monitoring systems are informed by them.
  The Objectives
• All development efforts in our lab platform team have clearly identified metrics tied to operational objectives and results
• All analyses are rigorous, reproducible, and follow best practices with contemporary tooling.
• Stakeholders can leverage self-support dashboards for analyses to help identify key measures of value within lab operations and enable team leads to make strategic decisions.
• Product team relies on a comprehensive, standardized reference data dictionary for lab operations concepts.

Experience, Traits, And Skills

• Demonstrated success working directly with stakeholders to understand complex processes and workflows.
• Experience leading and mentoring other quantitative analysts
• Experience conducting quantitative analyses for laboratory operations.
• Strong communication skills and business knowledge to translate quantitative results into meaningful recommendations for the business.
• Expert knowledge of product design frameworks and establishing key metrics for OKR and ROI calculations. Familiarity with interactive visualization tools, such as Tableau.
• Able to lead analysis on multiple projects at once, with multiple stakeholders.
• Experience building statistical models and applying them to operations workflows.
• Proficiency with SQL, R, and Python and code version control review tools, e.g. github
• Familiarity with Snowflake is a plus.