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

• Build and maintain machine learning models that predict the infrastructure cost impact of A/B experiments, translating experimentally observed signals (e.g., request volume changes) into business and system metrics (e.g. projected annualized costs) • Drive adoption of infrastructure metrics within the experimentation community through analysis, consultation with experiment owners, documentation, and training • Partner with platform teams (Observability, Experimentation Platform) to improve the quality and coverage of infrastructure usage data feeding our models • Extend our measurement framework to new metrics (e.g., latency) and new experiment types (e.g., infrastructure canary tests) • Champion an infrastructure lens within the broader experimentation community, helping shift culture toward reasoning about the full ROI and infrastructure impact of experiments • Connect with the larger analytics and experimentation communities at Netflix to bring visibility to our work and learn from others

At Allstate, great things happen when our people work together to protect families and their belongings from life's uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers' evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection. Job Description Arity is a part of the Allstate Corporation, which means we have the same innovative drive that keeps us a step ahead of our customers' evolving needs. We collect and analyze enormous amounts of data in order to provide cutting-edge solutions to companies invested in transportation. The Analytics Team Advertising data science team in Arity empowers the intelligence and efficiency of Arity Marketing Platform, which enables marketers to find their best customers wherever they are in their journey. Our goal is to empower marketers to reach prospects and display relevant messaging based on how, when, and where they drive. The team is responsible for optimizing the in-house marketing platform for advertisers and publishers, as well as our revenue, throughout all funnels. To achieve this, not only do we need to look for inefficiencies on the platform including individual campaigns, but also how to grow and scale the platform to gain more revenue. This means we need to find a cost-effective way to rapidly deploy, test and iterate algorithms on our platform. This team is also fully integrated within a cross-functional scrum structure including product owners, software engineers, ad operation team, and other talents to collaborate with to achieve the same business goals Where good people build rewarding careers. This Summer 2026 internship is for students currently enrolled in a graduate degree program. This role is responsible for building a proof-of-concept business solution using advanced reinforcement learning in Analytics and Data Science. This is a highly dynamic position that requires a great degree of flexibility, analytic agility and the ability to rapidly adapt to meet business need, new technologies and data sources. If you like the challenge of working on state-off-the-art techniques in advertising, this is the perfect job for you. Job Description Research and develop advanced machine learning methods to assess the technical feasibility and potential business value Utilizes effective project planning techniques to break down basic and occasionally moderately complex projects into tasks and ensure deadlines are kept Uses and learns a wide variety of tools and languages to achieve results (e.g., Python, SQL, machine learning libraries) Collaborates with the team in order to improve the effectiveness of business decisions through the use of data and machine learning/predictive modeling Executes on modeling/machine learning projects effectively Reviews and evaluates on appropriateness of techniques, given current modeling practices, to senior leadership Job Qualifications