Overview The Intuit Customer Success (ICS) Data Science & Analytics team is seeking a Manager 2, Data Science to lead product analytics for the platform that connects customers with our experts: how customers are routed and matched to the right expert, and how the knowledge and AI-powered tools experts use shape the outcome. This is a player-coach role. You will lead a small team of analysts and data scientists while spending a material part of your time staying hands-on in the data yourself, and you will be the analytics voice in the room with product managers and product development leaders, defining what "good" looks like before a feature ships and what evidence a change must produce to earn a rollout. Responsibilities
• Own the product analytics framework for the expert platform (event taxonomy, instrumentation requirements, adoption and funnel measurement), spanning how customers are matched to experts and how experts use the knowledge and AI tools available to them.
• Define the outcome metrics that make matching and tooling decisions comparable: time to connect, first-contact resolution, transfer and re-contact rate, satisfaction, conversion, and expert efficiency.
• Size initiatives before they are built; turning product problem statements into measurable impact estimates from behavioral data, separating total from achievable opportunity, and using those estimates to stack-rank the roadmap with product partners.
• Design and run the experimentation program, including the designs this domain requires: switchback, cluster-randomized, and interference-aware tests where routing one customer changes what is available to the next.
• Apply causal inference (difference-in-differences, propensity score, synthetic control, instrumental variables) to isolate product impact from shifts in demand mix, staffing, and seasonality.
• Track every launch after it ships: confirm the sized impact actually materialized, monitor it over time, and catch regressions before the business feels them.
• Evaluate AI-driven experiences, including matching and ranking models as well as retrieval and knowledge tools, against both offline quality benchmarks and online behavioral outcomes.
• Partner directly with product managers, product development leaders, and engineering on roadmap, launch readiness, and post-launch learning, as an equal voice rather than a reporting function.
• Drive data enablement for the domain: gap assessments, instrumentation requirements handed to engineering, metric definitions, data quality monitoring, and self-serve reporting.
• Set the technical bar for the team, review work in detail, and coach on both method and business framing. Qualifications
• 7+ years of analytics and data science experience,



with meaningful depth in product analytics for a software or AI product.
• Experience leading and developing analysts and/or data scientists while continuing to contribute hands-on.
• Demonstrated partnership with product management and engineering leaders through a full product lifecycle: discovery, launch, evaluation, and iteration.
• Experience sizing opportunities to prioritize a product roadmap
• Hands-on experimentation design and analysis, including guardrail metrics, variance reduction, and non-standard designs such as switchback or cluster-randomized tests.
• Hands-on causal inference experience, with sound judgment about when each method applies.
• Experience measuring AI, search, recommendation, or retrieval-based systems beyond accuracy alone.
• <[CDATA[Staff Software Engineer (SRE)
- Business Intelligence Overview Business Intelligence today spans a portfolio of Tier 1 and Tier 2 assets whose reliability, resiliency, and cost posture are owned ad hoc across individual app teams. We're hiring a dedicated SRE to be the single accountable owner for the health, availability, and operational excellence of these assets end-to-end — closing the gap between " best effort" reliability and a proven, tested, continuously improving one. this role sits horizontally across all bi scrum teams, partners tightly with our dba, is expected to build the automation and self-serve tooling that reduces the manual toil currently driving our incidents. Responsibilities Reliability Ownership
• Serve as the single accountable owner for the health, availability, and completeness of all Tier 1/Tier 2 BI assets, replacing today's diffused, ad hoc ownership across app teams.
• Keep asset health and resiliency practices current as CI/CD evolves — progressive rollouts, canary deployments, and failure mode and effects analysis (FMEA).
• Partner closely with the DBA to ensure system health is covered end to end (app layer + data layer), not siloed. Incident Readiness
• Design and run mock incidents / game days across BI assets to surface gaps proactively, before they become customer-facing.
• Own disaster recovery (DR) for every asset in the BI org: plan it, test it on a recurring cadence, and produce evidence it actually works (not theoretical DR).
• Build automation that removes manual, error-prone operational steps — the primary source of today's incidents and customer impact.



Engineering Excellence & Cost
• Own cloud resiliency, performance engineering, and security posture as a standing discipline across BI, not a side-of-desk responsibility for app teams.
• Establish and enforce operational and cost-optimization patterns across all assets (e.g., Splunk log volume/retention hygiene, right-sizing compute).
• Build reusable, self-serve libraries and tooling so individual teams stop re-solving the same reliability/ops problems independently.
• Track Intuit-wide platform and tooling initiatives (e.g., IKS, AIR) and drive horizontal adoption across BI so each team isn't independently absorbing migration cost. Qualifications Required
• 5+ years in an SRE, DevOps, or infrastructure/platform engineering role, ideally supporting data/BI or analytics platforms.
• Demonstrated ownership of production reliability for multiple services/assets — on-call, incident response, postmortems, and follow-through on remediation.
• Hands-on experience designing and executing disaster recovery plans (not just documenting them) — including recovery testing and validation.
• Experience with progressive delivery practices: canary deployments, blue/green rollouts, feature-flag-gated releases.
• Proficiency building automation/tooling (scripting or full software engineering) to eliminate manual operational toil.
• Working knowledge of cloud infrastructure cost management — right-sizing, log/data volume hygiene, resource tagging and chargeback.
• Experience with observability stacks (e.g., Splunk or equivalent) for both incident diagnosis and cost/volume management.
• Strong cross-team collaboration skills — this role influences without direct authority across multiple scrum teams and a DBA function. Preferred
• Experience running game days / chaos engineering exercises.
• Familiarity with FMEA or similar systematic risk-assessment methodologies.
• Exposure to Intuit-specific platforms (IKS, AIR) or comparable enterprise Kubernetes/AI infra migrations.
• Security posture / cloud security engineering background.
• Experience building self-serve internal developer platforms or shared libraries consumed by multiple teams. Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and advantages, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

📌 Senior Manager, Data Science (Bengaluru)
🏢 Intuit
📍 Bengaluru