Senior Strategy and Operations Analyst

Our Company Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours Senior Analyst, Enterprise Sales Analytics (IC) Location: Onsite/Hybrid — 3 days per week in office at NOIDA Team: Sales Operations & Enablement Employment: Full‑time Change how we sell, forecast, and retain—using first‑principles analytics. We’re scaling the analytics backbone that powers a global SaaS business. As a Senior Analyst (IC), you’ll turn messy go‑to‑market data into clear, decision‑driving insights—improving forecast accuracy, accelerating pipeline velocity, and reducing churn. You’ll collaborate with BI, Sales, Finance, Marketing, and Product to move the metrics that matter. What you’ll do (outcomes over activities) 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. Turn analysis into operating decisions 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. Influence without authority 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 Foundational skills (the must‑haves) 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. Experience 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: or Advanced degree in Math, CS, or Statistics + MBA or equivalent experience from a reputable institution. Nice to have 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). How we’ll measure success (Year 1 targets) Increase sales manager quota attainment by 30% through enhanced forecasting models. Reduce pipeline leakage by 20% via AI-powered deal prioritization. Cut customer churn by 15% through predictive retention analytics.