Senior Manager, Analytics (Customer Care)

Angi•Denver, CO

2d•Hybrid

About The Position

Angi's Care Analytics team is central to understanding, growing, and retaining its Pro network. The Senior Manager, Analytics will guide the analytical strategy and cadence that informs decision-making across Pro retention, churn, cash collection, and care team effectiveness. This role serves as the primary analytical partner to the customer care business unit, translating complex, weekly data signals into actionable narratives. The position involves leading a team of analysts, managing a dynamic project backlog, and setting the standard for analytical rigor across Customer Care Network metrics. It is a highly visible, high-impact role requiring proficiency in both SQL and presenting findings to leadership.

Requirements

6+ years of analytics experience
2+ years managing analysts in a fast-paced, data-intensive environment
Proven track record stewarding executive-facing reporting cadences (ideally weekly business reviews) with a strong ability to translate metric movements into business narratives
Expert-level SQL and proficiency with BI tools (Looker preferred)
Comfortable building and validating complex metric logic from scratch
Experience with retention/cohort analysis, churn modeling, or marketplace analytics
Strong statistical judgment: ability to distinguish meaningful trends from noise, apply regression analysis, and design structured investigations with clear hypotheses
Exceptional written and verbal communication—able to write tight, stakeholder-ready commentary (with appropriate caveats) under time pressure
Demonstrated ability to manage a dynamic project backlog, negotiate priorities with multiple stakeholders, and deliver on competing timelines
AI first mentality who leverages any tool available to increase efficiency, accuracy, and quality of insight generation

Nice To Haves

Experience in a marketplace, e-commerce, or home services business with a two-sided network (pro/consumer or seller/buyer)
Familiarity with Salesforce data structures and CRM-based analytics (save events, churn events, case history)
Experience with metric migration projects (e.g., moving reporting between data warehouses or unified explore environments)
Working knowledge of A/B testing and holdout experiment design
Exposure to AI/ML applications in analytics (coaching impact sizing, predictive churn, prospect scoring)
Jira or similar project tracking experience for managing analytical work intake

Responsibilities

Steward and publish weekly business reviews, synthesizing key metrics and signals into clear, stakeholder-ready commentary.
Flag material metric movements proactively, distinguishing noise from signal, and recommend whether to investigate immediately, monitor, or backlog.
Lead the Weekly Metrics review for senior leadership, including narrative development and proactive identification of metric gaps in retention trends.
Manage transitions of report stewardship within the team and ensure analytical continuity.
Run a weekly Care Analytics prioritization process: maintain the backlog across multiple stakeholder groups, arbitrate competing priorities, and communicate trade-offs clearly.
Manage and develop a team of analysts, providing coaching on both technical craft and business communication.
Define project scopes and ETAs, track delivery, and surface blockers early, including upstream data/engineering dependencies.
Champion the Analytics + AI literacy initiative, helping team members build skills in AI-assisted analysis and productivity tooling.
Drive structured investigations into metric deterioration using regression analysis, cohort segmentation, and hypothesis testing to isolate drivers of key metrics and distinguish correlation from causation.
Partner with Data Science and Product Analytics on metric definitions, unified views for migrations, and new metric builds.
Validate and QA metric definitions end-to-end, including numerator/denominator alignment, data source mapping, and appropriate use of segment filters.
Act as the analytical voice in leadership discussions on customer metrics, rules, program effectiveness, and optimization strategies.
Translate ambiguous asks from business leaders into structured analytical plans with defined hypotheses, recommended cuts, and timelines.
Collaborate cross-functionally with Care Ops, Finance, Product, and Data Engineering to ensure analytical outputs are grounded in operational reality.
Partner with Data Engineering on data quality issues to ensure reporting reliability.