• Lead complex, multi-signal investigations (e.g., account takeover, automation, fraud rings, API abuse) and produce clear, actionable remediation plans. • Serve as the CX technical authority in escalations, guiding hypothesis formation, analysis strategy, and root-cause conclusions. • Translate customer context (risk tolerance, user base, operational constraints, compliance) into measurable detection and tuning objectives. • Identify emerging fraud patterns and cross-tenant trends; conduct quarterly intelligence briefs for customers and recommended mitigations. • Design and recommend detection strategies combining rules, features, and (where applicable) model-driven scoring to reduce fraud while controlling false-positive costs. • Develop reusable feature definitions and tuning approaches that can be applied across customers (not only one-off tenant fixes). • Partner with engineering and products to shape product roadmap priorities, improve explainability and investigation tooling (e.g., “why did this fire,” attribution, drill-down paths). • Define and own CX measurement systems: detection quality, false-positive impact, operational metrics, customer outcomes, and performance over time. • Lead offline evaluation and backtesting methodologies for fraud controls and customer configuration changes. • Drive experimentation practices (A/B where feasible, quasi-experimental, pre/post with controls) and ensure results are decision-grade. • Detect data drift and performance degradation signals; propose retraining triggers and/or mitigation plans in partnership with ML/Engineering. • Create “investigation packs” for R&D: evidence, root cause hypothesis, recommended technical changes, and measurable acceptance criteria. • Mentor and develop other DS/analysts on CX through playbooks, code reviews, investigation standards. • Partner with GTM teams to quantify customer value, communicate outcomes, and support renewals/expansions through data-backed narratives.

• Design and lead the development of various artificial intelligence initiatives to help improve health and wellness of patients • Work with the business leaders and customers to understand their pain-points and build large-scale solutions for them. • Define technical architecture to productize Innovaccer’s machine-learning algorithms and take them to market with partnerships with different organizations • Proven ability to break down complex business problems into machine learning problems and design solution workflows. • Work with our data platform and applications team to help them successfully integrate the data science capability or algorithms in their product/workflows. • Work with development teams to build tools for repeatable data tasks that will accelerate and automate development cycle. • Stay updated on advancements in AI/ML technologies and apply them to enhance existing systems.

• 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