• Drive discovery and translational projects in collaboration with AI and internal research teams with a primary focus on integrating scRNA-seq and spatial biology into Natera’s pipeline. • Collaborate with external translational teams on building single-cell and spatial biology workflows into their early phase clinical studies. • Leverage novel technologies to inform collaborative reverse and forward translation efforts as well as internal target discovery and validation projects. • Design and execute lab-based research programs that bridge clinical observations with mechanistic hypotheses, supporting asset progression and CDP expansion • Lead and contribute to technology area team focused on single cell genomics and spatial omics, building out multiomics capabilities to support foundational models and RWD efforts • Partner with internal and external collaborators to establish, optimize, and deploy cutting-edge platforms for translational applications • Serve as an internal expert and thought leader on emerging genomics technologies relevant to oncology drug discovery and development • Represent translational science on life cycle, RWD, and BioPharma BD teams • Collaborate with Clinical, Biomarker, Discovery, and Regulatory colleagues to shape clinical trial design and biomarker plans • Build and maintain external scientific partnerships and advisory relationships as appropriate • Lead and mentor a team of scientists and contractors, with a strong commitment to career development and scientific growth • Foster a collaborative, high-performance team culture with clear goals and accountability

• Lead R&D work at Afresh for the development and performance of AI/ML models that power replenishment technology. • Model consumer demand, item-level perishability, and complex multi-echelon supply chains. • Drive fundamental changes to the core system from research through production, writing rigorously tested and scalable code. • Raise the technical bar across the Intelligence team: mentor scientists and engineers, set standards for experimental rigor, and review designs and results. • Push the boundaries of AI capabilities in both products and scientist workflows.

• Set technical direction for core replenishment R&D — define the modeling roadmap across demand forecasting, inventory optimization, and decision-making policy, and align it with product and business strategy. • Model complex problems such as inventory decay, promotions, price elasticity, and inventory uncertainty, and implement solutions to multi-stage and multi-echelon inventory optimization problems. • Drive fundamental changes to our core system from research through production, writing rigorously tested and scalable code. • Lead research and development for new product and business challenges. • Raise the technical bar across the Intelligence team: mentor scientists and engineers, set standards for experimental rigor, and review designs and results. • Push the boundaries of AI capabilities in both products and scientist workflows.

• Own the full data science engine for a priority vertical, from business problem to deployed model to live ROAS performance, driving measurable revenue and media efficiency • This is a hands-on, in-the-weeds role: you are heavily immersed in the data and the modeling, framing the business problem directly with stakeholders, building and validating the model, handing the ML-engineering last mile to your ML engineering partner, and staying engaged through deployment, monitoring, and performance analysis • You will start focusing on Insurance and Advertiser Quality, with scope that broadens over time • Your primary metric is ROAS • Own the Insurance vertical's primary modeling work end-to-end with measurable ROAS impact • Deliver buying models that maintain positive ROAS and quality • Drive lead quality improvements across our portfolio of brands: Messaging, Funnels, Content/Listicles, and more resulting in measurable impact to revenue growth • Establish trusted, direct partnership with vertical business stakeholders • Produce trusted output: validated, documented, low correction burden • Identify and leverage net-new modeling opportunities the business has not flagged