• Contribute to the development and optimization of analysis modules used to support clinical trial decision-making. • Collaborate with data scientists to refine workflows that integrate proteomics, clinical endpoints, and biological interpretation. • Develop pathway enrichment approaches tailored to SomaScan and Olink proteomic platforms. • Incorporate causal mediation analysis methods into the clinical omics framework. • Evaluate how treatment interventions may influence clinical outcomes through intermediate molecular changes.

• Develop and implement the company’s data science strategy • Identify opportunities to leverage data for business value • Take responsibility for the full technical stack for development and deployment • Design, build and maintain scalable machine learning models and efficient data pipelines • Collaborate with cross-functional teams to gather requirements

The Faculty Advisor for Sciences is a key member of the Faculty Advisory Team within the Pearson North America Higher Education Customer Success Organization. They will strategically identify and partner with key course-level administrators, acting as a pedagogical and subject-matter expert on the alignment of Pearson digital products with instructors’ learning objectives and student outcomes. Expertise and experience in teaching Physics at the college or university level Expert-level knowledge of Pearson digital implementation models within courses related to their own teaching/research field Investigate and respond to questions, offering tailored responses to specific audiences and purposes Develop and utilize a set of milestones to effectively track growth and success Collaborate with other teams to provide insights and feedback from the perspective of an instructor Equip faculty at pre-identified accounts with necessary skills and information to maximize the impact of Pearson digital products Facilitate face-to-face and virtual trainings or workshops Create implementation-specific product tutorials or videos

• Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools. • Design and implement graph-based models to analyze, optimize, and improve supply chain networks. • Apply advanced data science techniques to identify patterns, inefficiencies, and bottlenecks across logistics and operations. • Build scalable data pipelines and analytical models using PySpark for large-scale supply chain datasets. • Develop predictive and prescriptive models to support decision-making in areas such as demand forecasting, routing, and inventory management. • Collaborate with cross-functional teams including operations, product, and engineering to translate business challenges into analytical solutions. • Communicate insights and recommendations clearly to stakeholders through data storytelling, visualizations, and presentations. • Share your passion for Data Science with the broader enterprise community; identify and develop long-term processes, frameworks, tools, methods and standards. • Collaborate, coach, and learn with a growing team of experienced Data Scientists. • Stay connected with external sources of ideas through conferences and community engagements