• Roadmap Influence: Synthesize marketplace dynamics, user behavior, and experiment signals into clear recommendations; influence major roadmap decisions. • Goal Setting: Partner with Product, Design, and Engineering leaders to co-create goals. Ensure every feature has an impact plan. • Executive Storytelling: Distill complex marketplace data into crisp, strategic narratives to drive alignment on "where to play and how to win." • Center of Excellence: Lead Gametime’s experimentation design and strategy. Standardize how we run A/B and multivariate tests to ensure we are learning at the speed of the marketplace. • AI & Personalization Quality: Co-own the data-driven design of AI features (e.g., personalized event recommendations). Establish benchmarks for AI quality to ensure relevance and conversion. • Self-Serve Enablement: Drive product velocity by building self-serve capabilities, allowing PMs and Designers to access real-time insights without a bottleneck. • Fan Journey Mapping: Deeply understand the fan's path from app-open to ticket-scan. Identify friction points in the checkout and discovery flows to increase conversion. • Retention & LTV: Build the frameworks to measure and influence long-term retention and customer lifetime value, moving beyond one-off purchases to habitual usage. • Instrumentation Governance: Oversee the data quality and tracking strategy (e.g., Hightouch) to ensure our product data is the "source of truth."

• Own end-to-end analytics for growth marketing, partnering with Marketing, Finance, and external partners to understand user behavior, define target audiences, measure campaign performance, and forecast and allocate budgets • Translate ambiguous business questions into rigorous analyses and clear recommendations that improve individual campaign performance and optimize the overall marketing channel mix • Design, execute, and interpret experiments and quasi-experiments to enable causal inference and inform high-stakes marketing investment decisions • Partner closely with MarTech, Product Management, and Engineering to scope, build, and evaluate new capabilities—such as developing local market intelligence framework and improvements to customer LTV bidding systems—ensuring measurable performance gains • Develop deep domain expertise in growth marketing measurement, experimentation, customer segmentation, and targeting optimization, and apply that expertise to influence strategy and execution • Collaborate with cross-functional partners to build scalable analytical solutions, tools, and frameworks that unlock sustained growth • Raise the bar for Thumbtack’s data culture by designing, maintaining, and documenting high-quality, trusted data tables and metrics that enable self-serve decision-making across teams

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Kickstart Your Data Science Journey with a Paid Internship! Are you passionate about turning data into insights? Ready to work on real projects, harness the power of data to fuel innovation, and make a meaningful impact on business success? We’re thrilled to offer a paid Data Science internship where you will integrate with an existing project team to gain hands-on experience in a breadth of data science and software development areas. If you’re eager to sharpen your skills, solve complex problems, and be part of a dynamic team, this is the opportunity you’ve been waiting for! The Illumination Works 2026 Internship Program runs from May 2026 to August 2026. Start and end dates within those months are flexible, ensuring a minimum internship duration of 10 or more weeks. Please note your availability, including start, end, and any vacation days/weeks on your Cover Letter. - Extract, cleanse, and preprocess structured and unstructured data from multiple sources - Conduct advanced analytics leveraging predictive modeling, machine learning, simulation, optimization, and other techniques to deliver actionable insights for business objectives - Fine-tune, train, test, and apply machine learning algorithms, natural language processing (NLP), and computer vision techniques, time-series modeling, feature extraction, feature similarity clustering, and others, to address business challenges - Communicate findings and present results to the project team, leadership, and fellow interns, providing clear recommendations and next steps Qualifications - Graduate student currently enrolled in an MS or PhD program OR a Senior or recent graduate in a 4-year university pursuing a degree in a relevant field (i.e., Computer Science, Information Technology, Statistics, Artificial Intelligence, Data Science, Analytics, Mathematics, Engineering, or other related scientific field) - Minimum GPA of 3.6 on a 4.00 scale - Experience with the Python programming language, including using Python’s data/data science/machine learning libraries such as PySpark, Pandas, Numpy, Scipy, Scikit-learn, Gensim, nltk, spaCy, OpenCV, PyTorch, and/or Tensorflow - Knowledge of and experience with computer vision (CV), natural language processing (NLP), and/or sensor analytics (IIoT/IoT) - Outstanding problem-solving and critical thinking skills - Innovative, creative, out-of-box thinking with willingness to learn new skills - Strong verbal and written communication skills - Proficiency in MS Office Suite (Word, Excel, PowerPoint) - Acceptable candidates must successfully pass a drug test, pass a background screen, and provide official transcripts - US Citizenship Requirements - Experience with collaborative coding and source control tools (e.g., Databricks, GitHub) - Experience working in an agile framework with tools such as Jira - Experience with data engineering and databases (SQL and/or NoSQL) Application Requirements - Resume with GPA - Cover letter with why you are interested in the internship, why you feel you are the right fit for the internship, your goals for the internship, and your schedule availability - Letter of reference (strongly encouraged)

• Partner with the AI Technical Product Manager to support data-driven decision-making across AI initiatives • Analyze datasets to assess the potential functional and business impact of proposed features • Define success metrics and measure post-implementation performance to evaluate actual impact • Provide actionable insights that inform product roadmap, prioritization, and ROI • Collaborate with ML Engineers to identify machine learning opportunities using proprietary datasets • Design and execute experiments (A/B testing, hypothesis testing) to validate product and AI feature effectiveness • Build dashboards, reports, and visualizations to communicate insights to technical and non-technical stakeholders • Ensure data quality, integrity, and proper usage across projects.