Data Analytics Manager - Pod Spoc

EXL is considered the Special Investigation Unit by 6 of the top 10 US health insurance companies (~1/3rd of US healthcare data is handled by us), helping with error/overpayment detection of hospital/doctor claims. Unlike typical services and consulting companies, we make our revenue from the savings we identify for the client (Commission/Outcome basis). We productize algorithms and R&D accelerators that are intended to be used across multiple health insurance clients for the above business case.

The POD SPOC is accountable for end-to-end analytics project delivery for an assigned client POD. This is a hands-on delivery role that converts client and program priorities into executable analytics workplans, manages project cadence across teams, governs POD and program health through KPIs, and ensures the analytics portfolio contributes to monthly, quarterly, and annual revenue goals.

*Base Pay Range: 93,900 - 154,200

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits

• 10+ years Managing analytics delivery, program analytics, client delivery, KPI governance, or similar hands-on analytical roles
• 7+ years Data profiling, joins, reconciliations, validation, trend analysis, business-rule testing, and performance-aware querying
• 3+ years Data wrangling, automation, exploratory analysis, QA checks, reusable scripts, and productivity improvements
• 2-3 years Notebook-based analysis, data exploration, validation, collaborative code review, and scalable analytics workflows
• 8+ years Complex formulas, pivots, QA trackers, KPI packs, reconciliations, and stakeholder-ready analysis views
• 5+ years Executive summaries, KPI narratives, revenue readouts, client-ready decks, and business recommendations
• 5+ years Trend interpretation, sampling logic, variance analysis, outlier detection, opportunity sizing, and decision support
• 5+ years POD scorecards, program health dashboards, delivery metrics, corrective actions, and operating cadence
• 5+ years Workplans, action logs, dependency tracking, risk management, release coordination, and stakeholder follow-up
• Basic practical knowledge of ML/LLM

Preferred Background:

• Hands-on experience managing analytics delivery for healthcare, payment integrity, claims operations, audit analytics, risk analytics, finance analytics, or operational analytics programs.

• Experience managing one complex client or two to three smaller clients while balancing BAU production, enhancements, strategic projects, and revenue priorities.

• Prior consulting, advisory, transformation, or client-facing analytics delivery experience is strongly preferred and will be considered an added advantage.

• Experience leading cross-functional initiatives with distributed teams across onshore and offshore locations.

• Experience creating KPI packs, program scorecards, operational dashboards, revenue summaries, executive updates, and business case materials.

• Exposure to AI, ML, GenAI, or LLM-enabled analytics solutions, productivity tools, or automation initiatives.

• Comfortable working with senior stakeholders while also being hands-on with data validation, analytical review, and issue diagnosis.

*The posted base salary range is the hiring base salary range for this role. Final offers are based on several factors, including: the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position.

Hands-on Analytics Project Delivery:

• Own the delivery plan for analytics projects, enhancements, recurring reporting, and ad hoc client asks within the assigned POD.

• Translate business problems into analytical requirements, define scope, estimate effort, align timelines, and manage dependencies across analytics, Tech team, Operations, POD, and program teams.

• Remain hands-on with analysis when required, including data profiling, validation, reconciliation, metric interpretation, issue diagnosis, and quality review before release.

• Track deliverables from intake through development, validation, deployment, stabilization, and post-release monitoring.

• Identify delivery risks early, escalate blockers with clear options, and drive closure through structured follow-ups and action logs.

• Ensure outputs are accurate, audit-ready, clearly documented, and consumable by business, program, and client-facing teams.

POD and Program Health KPI Governance:

• Maintain a single view of POD health using KPIs such as delivery timeliness, backlog aging, production quality, rework, issue closure, automation adoption, revenue realization, client priority execution, and operational throughput.

• Prepare recurring KPI reviews that highlight status, risks, root causes, corrective actions, owners, and due dates.

• Diagnose metric movement using data and hands-on analysis rather than reporting trends at a surface level.

• Define practical action plans when POD metrics move off track and coordinate with the right teams to restore performance.

• Support program-level governance by consolidating insights across project status, savings/revenue performance, operational throughput, and client commitments.

• Create executive-ready summaries and PowerPoint narratives that explain metric movement, delivery risk, and business impact.

Revenue and Business Outcome Ownership:

• Treat revenue as the primary success goal across monthly, quarterly, and annual horizons.

• Partner with program, finance, operations, and client teams to track forecast versus actual revenue or savings contribution from analytics initiatives.

• Prioritize analytics work based on expected business value, revenue impact, client commitments, compliance needs, and delivery feasibility.

• Surface revenue leakage, underperformance, stalled initiatives, delayed implementation, and dependency risks that may affect planned outcomes.

• Support leadership with concise revenue drivers, risks, recovery actions, and opportunity areas for assigned client PODs.

• Build practical business cases for new analytics initiatives, automation opportunities, and value realization improvements.

Stakeholder Coordination and Project Management:

• Act as the day-to-day coordination point for the POD, ensuring that program stakeholders, technical teams, validation teams, operations teams, and client-facing teams stay aligned.

• Operate like a project manager when needed by running structured project discussions, maintaining workplans, tracking action items, capturing ownership, and following up to closure.

• Manage competing priorities and negotiate realistic timelines when client asks, production needs, revenue priorities, and strategic initiatives overlap.

• Communicate status clearly using delivery trackers, issue logs, KPI dashboards, Excel summaries, PowerPoint updates, and leadership readouts.

• Build trusted working relationships across internal teams and represent the POD from Analytics team in cross-functional forums.

Analytics Execution and Technical Contribution:

• Use SQL to profile data, validate extracts, reconcile counts, identify trends, and support audit-rule or business-logic analysis.

• Use Python for data preparation, exploratory analysis, automation, quality checks, productivity improvements, and root-cause analysis.

• Work in Databricks environments for data exploration, validation, code review, notebook-based analysis, and scalable analytical solution development.

• Apply statistics to evaluate trends, anomalies, performance drivers, variances, sample results, and business opportunity sizing.

• Use Excel for hands-on analysis, validation, tracker management, KPI summaries, pivots, formulas, and stakeholder-friendly views.

• Use PowerPoint to convert analysis into crisp business updates, revenue narratives, KPI readouts, and executive-level stories.

• Apply basic awareness of ML and LLM concepts to identify practical automation, summarization, documentation, quality-control, and insight-generation opportunities.

Consulting Style Problem Solving:

• Operate with a consulting mindset by structuring ambiguous problems, developing hypotheses, validating with data, and providing clear recommendations.

• Challenge assumptions constructively and use data to influence prioritization, delivery decisions, and stakeholder alignment.

• Translate complex analytics into simple business messages that connect work effort, client impact, operational performance, and revenue outcomes.

• Prior consulting, advisory, transformation, or client-facing delivery experience is an added advantage.