• Data literacy made easy through practical, hands-on training • Flexible, modern online courses in Data Science, Business Analytics and Artificial Intelligence • Discussion of business processes and optimization of decision-making • Building AI models and automating processes

• Dig into experiment results to surface high-impact personalization opportunities; translate what you find into crisp scientific problem formulations that balance rigor with speed-to-learning. • Work closely with product managers, engineers, operations, legal, and privacy partners to align on ML requirements, de-risk design decisions, and gather requirements on explainability and compliance. • Hands-on develop, evaluate, and ship ML models and data pipelines at scale—batch and real-time, structured and unstructured—using Airbnb’s paved-path tooling and AI native mindset • Prototype and iterate quickly: turn a new idea into a working model in a prototype, get early signals from an experiment, then productionize what works. You move fast and don’t wait to be asked. • Present findings and proposals at team reviews and to technical, product, and executive stakeholders—making complex ML results legible without dumbing them down, and generating conviction on the roadmap ahead. • Stay current with the research community; draw on state-of-the-art advances in recommendation systems, LLMs, and personalization to raise the bar for what the team ships. Occasionally publish externally or present at conferences to advance Airbnb’s scientific standing.

• You will drive the ongoing development and enhancement of TransUnion’s in-house Insurance Analytics platform (InsureR), applying advanced expertise in machine learning, mathematical programming, and software development to deliver scalable, high-quality solutions. • You will enhance the usability and effectiveness of analytic tools by contributing to UI/UX improvements that enable better user experiences and adoption. • You will provide hands-on regional support, partnering with teams to troubleshoot and resolve issues within local analytic environments, ensuring continuity and performance. • You will play a key role in supporting global Data Science and Analytics (DSA) and Insurance Analytics teams, strengthening critical capabilities that power enterprise-wide analytics solutions. • Lead the development of analytic solutions using languages such as C++, R, Python, SQL, Hive and Spark, formalizing some of these efforts into repeatable process improvements. • Assist Global Technology with maintenance of the tools and frameworks used by analysts on the high-performance computing (HPC) cluster and be a lead representative of the Data Science Development team in projects led by Global Technology as a subject matter expert on machine learning and scientific computing. • Own data science consulting responsibilities for a variety of regions, working to identify strategies and opportunities to test and adopt TransUnion’s analytic products and services. • Contribute to research and innovation initiatives in collaboration with other DSA peers and may lead small analytic research teams or manage research interns on a project basis, as needed. • You will participate in the mentoring and training of junior colleagues, fostering a high-performance culture and cultivating an environment that promotes excellence and reflects the TransUnion brand.

• Lead and develop a team of Product Data Analysts and an embedded Data Scientist through coaching, prioritization, and hands-on analytical leadership • Establish clear expectations and quality standards for product analytics, experimentation, and data science work • Build scalable operating processes for intake, prioritization, stakeholder communication, and cross-functional collaboration • Partner with Product and Engineering leaders to support data-informed product strategy and decision-making • Help teams connect product behavior to activation, adoption, engagement, retention, and customer value • Guide experimentation strategy and ensure rigorous A/B testing methodologies and actionable interpretation of results • Improve product metrics and scorecards that measure product health, workflow usage, launch performance, and customer outcomes • Enable thoughtful self-service analytics through tools such as Metabase, Amplitude, Secoda, and dbt • Partner cross-functionally with Analytics Engineering, Data Engineering, Engineering, and ML/AI teams to improve ownership clarity and data quality • Translate complex analysis and data science work into clear, decision-oriented insights for technical and non-technical stakeholders • Identify practical, high-impact opportunities for predictive modeling, classification, recommendation systems, and AI-enabled product intelligence • Help stakeholders distinguish between meaningful data science opportunities and lower-value or overly speculative work • Contribute directly to high-priority analyses, ambiguous product questions, and strategic initiatives when needed • Protect the team from low-leverage work while creating space for higher-impact analytical and strategic contributions