At DraftKings, AI is becoming an integral part of both our present and future, powering how work gets done today, guiding smarter decisions, and sparking bold ideas. It's transforming how we enhance customer experiences, streamline operations, and unlock new possibilities. Our teams are energized by innovation and readily embrace emerging technology. We're not waiting for the future to arrive. We're shaping it, one bold step at a time. To those who see AI as a driver of progress, come build the future together. The Crown Is Yours As a Lead Analyst on the Trading Analytics team, you will play a key role in understanding and optimizing how DraftKings participates across prediction-market-style exchanges. You'll partner closely with trading, product, data science, and engineering to evaluate exchange performance, monitor liquidity, and build analytical frameworks that guide our strategy. Your work will strengthen execution quality, pricing consistency, and overall marketplace health. What you'll do as Lead Analyst, Trading Analytics - Assess trading exchange performance by analyzing post-fill customer behavior, latency impacts, and where there might be delays or inefficiencies. - Build analytical frameworks that simplify exchange dynamics into clear, repeatable metrics that guide trading decisions. - Map our liquidity footprint across exchanges, including contract coverage, trading volumes, and performance trends. - Partner with sports traders and trading operations to diagnose issues, improve liquidity allocation, enhance pricing accuracy, and elevate customer experience. - Develop dashboards and reporting that provide real-time visibility into exchange quality and our performance across trading environments. - Deliver insights that inform how we allocate liquidity, structure quoting behavior, and evolve our exchange participation strategy. What you'll bring - At least 5 years of experience in analytics or data science. Experience within sportsbook trading, prediction markets, or financial market microstructure is a plus. - Bachelor's degree or equivalent in Mathematics, Statistics, Economics, Computer Science, Engineering, Business Analytics, or another relevant discipline. - Ability to take on complicated problems and turn them into simple, analytical frameworks. - High proficiency in SQL, Excel, and Tableau (or similar visualization tools). - Experience with R, Python, or another statistical programming language is a plus. - Familiarity with exchange dynamics such as order books, liquidity, fills, and execution quality is a plus. - Ability to thrive in a fast-paced, results-driven environment. #LI-AS1 Join Our Team We're a publicly traded (NASDAQ: DKNG) technology company headquartered in Boston. As a regulated gaming company, you may be required to obtain a gaming license issued by the appropriate state agency as a condition of employment. Don't worry, we'll guide you through the process if this is relevant to your role. The US base salary range for this full-time position is 121,000.00 USD - 151,300.00 USD, plus bonus, equity, and benefits as applicable. Our ranges are determined by role, level, and location. The compensation information displayed on each job posting reflects the range for new hire pay rates for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific pay range and how that was determined during the hiring process. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

• Help Product Teams Make Data-Informed Decisions • Analyze product usage and customer journey data to identify meaningful patterns and opportunities to improve product outcomes • Partner with product managers to identify key product questions and ensure analysis and reporting support product planning, prioritization, and decision-making • Translate complex data into clear, actionable insights that inform product decisions • Contribute analysis that helps teams evaluate trade-offs, risks, and opportunities • Measure Product Performance and Impact • Implement measurement plans for new features, experiments, and product initiatives • Analyze product adoption, engagement, and usage patterns • Help evaluate whether product changes achieve intended customer and business outcomes • Surface opportunities to improve product performance based on data trends • Build Dashboards and Reporting • Develop and maintain dashboards that track product health and key performance metrics • Ensure dashboards are clear, intuitive, and aligned with shared metric definition • Create repeatable reporting that enables teams to independently monitor performance • Partner with stakeholders to ensure reporting supports real decision-making needs • Enable Teams to Use Product Data • Coach product teams through interpreting analytics dashboards and reports • Provide guidance on how to use product analytics tools and metrics effectively • Create documentation and usage guidance that enables self-service reporting • Provide day-to-day support on product data, metrics, dashboards, and analytics tool usage • Promote Reliable and Trustworthy Product Data • Model established standards for product data collection, metrics, and reporting • Help identify and resolve data quality issues • Partner with business intelligence, data engineering, and other data teams when product data needs alignment with enterprise reporting • Contribute to clear documentation of data sources, data definitions, and metrics • Work Across the Product Data Ecosystem • Analyze product data using tools such as Pendo, Power BI, and Snowflake • Analyze and incorporate data from product development tools such as Jira to help teams understand delivery progress and product outcomes • Collaborate with product, UX research, and business intelligence partners to connect product insights with broader organizational data

• Own Upstream’s enterprise data, analytics, and AI strategy • Define and execute a 3-year analytics roadmap aligned to revenue, cost, and quality KPIs • Lead, mentor, and grow a team of 4–6 analysts and engineers • Design and deploy enterprise dashboards, predictive models, and automated insights using Microsoft Power BI + Fabric • Integrate data from EHR (RainTree), RCM, etc. • Implement real-time analytics using Azure Data Lake + Synapse • Champion AI/ML adoption (e.g., demand forecasting, therapist scheduling, outcome prediction) • Foster cross-functional partnerships (Clinical, Revenue Cycle, Operations, Finance)

• Live by and champion all of our core values (#ownership, #empathy, #day-one, and #humility). • Build and maintain durable, documented, tested models in dbt that power GTM reporting (e.g., pipeline, funnel, conversion, bookings, forecasting, rep performance, account health). • Design dimensional models (facts/dimensions), metrics definitions, and “one version of the truth” datasets to make analysis repeatable and trustworthy. • Develop and maintain Looker explores, dashboards, and standardized reporting that enable self-serve by the GTM organization. • Implement data quality checks, monitoring, and model documentation so stakeholders can confidently act on results. • Partner with data engineering on upstream improvements (instrumentation, source system hygiene, pipeline reliability). • Collaborate with GTM stakeholders to clarify business questions and translate them into analysis plans, metrics, and decision frameworks. • Perform analysis using SQL (and Python as needed) to identify trends, diagnose performance, and recommend actions (e.g., funnel bottlenecks, win-rate drivers, deal velocity, territory/segment opportunities). • Build GTM narratives that connect data to decisions—clearly communicating insights with business context to leaders and cross-functional partners. • Proactively monitor performance and adoption signals to surface risks, anomalies, and opportunities—then drive stakeholders toward resolution. • Identify high-leverage GTM “insight bottlenecks” and design data products to solve them (e.g., standardized KPI hubs, automated pipeline risk alerts, self-serve funnels, guided explorations, executive views). • Leverage LLMs/AI to accelerate insight workflows—examples might include: • AI-assisted “metric explainers” embedded into dashboards • Natural-language Q&A experiences over curated GTM datasets • Automated insight summaries / anomaly narratives for weekly GTM reviews • Guided self-serve templates (“answer this question in 3 clicks”) for common GTM use cases. • Establish success metrics for data products (e.g., reduced time-to-insight, increased self-serve usage, fewer ad-hoc requests, improved forecast accuracy). • Work independently in ambiguous environments—framing problems, generating hypotheses, and leading toward outcomes. • Manage multiple priorities and communicate clearly through Slack, docs, and live stakeholder readouts. • Contribute to team standards (modeling patterns, documentation, code review, stakeholder intake). • Mentor other analysts through collaboration, query/design feedback, and best practices (where applicable to your team structure).