Advanced Analytics & Reporting Analyst 3

Own the revenue intelligence foundation

• Model the system: Build clean, reusable datasets and semantic layers across CRM, customer, and product signals. Define metric logic (pipeline coverage, conversion, forecast error, churn risk) with clear, durable definitions.
• Quantify risk and upside: Apply statistical methods and ML where they add signal time series for forecasting, classification/uplift for deal and renewal prioritization, survival analysis for churn.
• Automate the feedback loop: Productionize data quality checks, anomaly detection, and alerting so insights flow into weekly operating rhythms without manual chase.

• Forecast with precision: Diagnose variance and bias, and propose specific changes to roll ups, cadence, and judgment overlays that improve commit reliability.
• Fix funnel friction: Identify stage to stage drop offs, recommend process changes (enablement, handoffs, definitions), and quantify impact via pre/post or controlled tests.
• Craft compelling narratives and visuals suitable for executives, ensuring clarity and ease of reuse during QBRs.

• Partner across the aisle: Work with Sales leaders, Finance, and Marketing to land metric contracts and action owners.
• Improve: Mentor peers on analytical structure, code hygiene, and communication craft; contribute templates, queries, and documentation others can build on.

What you bring

• Data engineering: Strong SQL; experience crafting schemas for analytics (slowly changing dimensions, surrogate keys, late arriving facts), building reliable pipelines/orchestration, and version controlling code and data definitions.
• Statistics: Comfort with inference and experimentation (A/B, diff in diff, power), regression/time series, uncertainty communication, and translating assumptions into business guardrails.
• Machine learning (pragmatic): Hands on with supervised learning for classification/regression and survival/retention modelling; ability to choose simple, explainable models when they outperform complexity.
• Business and communication: You map models to money (quota, coverage, conversion, churn) and package insights into crisp, executive ready narratives.

• 5 8 years in SaaS Sales/Revenue Operations analytics; proven impact through measurable results.
• Proven track record operating across modern data stacks (warehouse + transformation + notebooks + BI) specific tools are less important than the architectural thinking and craft you bring.
• Education: B.Tech/BE or Advanced degree in Math, CS, or Statistics + MBA or equivalent experience from a reputable institution.

• Experiment development in go-to-market settings (policy changes, enablement tests, pricing/packaging experiments).
• Exposure to MLOps concepts (feature hygiene, drift checks, monitoring) and documentation culture (readmes, metric contracts, data dictionaries).

• Increase sales manager quota attainment by 30% through enhanced forecasting models.
• Reduce pipeline leakage by 20% via AI-powe'red deal prioritization.
• Cut customer churn by 15% through predictive retention analytics.