• Collaborate with the product and engineering team to identify areas for automation and improvement • Designing and developing state-of-the-art NLP algorithms, with a focus on orchestrating LLMs across various providers (OpenAI, Anthropic, Google, etc.) to optimize for cost, latency, and accuracy. • Analyse data and conduct experiments to improve the accuracy and efficiency of models • Build and deploy models that can be integrated into production applications • Evaluate emerging LLM tools and frameworks to maintain a competitive edge in research automation • Collaborate with leading ML practitioners in the research synthesis space to fundamentally change the way the world derives accurate insights from global research output

• Partner with cross-functional teams - including product management, design, engineering, research - to support product development efforts • Experiments: design and measure experiments to inform feature decisions • Develop key strategic insights through exploratory data analysis, to inform future investments or pivot in strategy • Build scalable metrics and dashboards to empower efficient decision-making at Nextdoor • Participate in in-person Nextdoor events such as trainings, off-sites, volunteer days, and team-building exercises • Build in-person relationships with team members and contribute to Nextdoor's company culture

• 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.