The opportunity
GTM Operations & Strategy at MongoDB is a global team of builders and innovators focused on unleashing MongoDB’s sales greatness by pairing world‑class analytics with scalable operations. Within GTM Operations, the GTM Intelligence – Applied Science team turns complex GTM data…
What you'll do
Translate GTM questions into analytical projects
Partner with GTM Ops, Sales Strategy & Planning, Sales Leadership, and: Central Analytics to scope problems, define success criteria, and prioritize work across areas like segmentation, territory design, account prioritization, and pipeline/forecast health.
Structure ambiguous questions into hypotheses, analytical plans, and clear: recommendations for senior stakeholders (SVPs, RVPs, functional leaders).
Design and build scalable analytics & models
Develop and maintain statistical and machine learning models (e.g., NARR: prediction, deal qualification, account momentum, workload identification) that inform forecast expectations, territory assignments, and deal prioritization.
Engineer robust data pipelines and features (SQL/Python) on top of our GTM: data stack (Salesforce, product usage, call transcripts, marketing signals, etc.) in partnership with data and platform teams.
What they're looking for
- ~4–7+ years in analytics, sales operations, data science, or: strategy/consulting roles supporting B2B or SaaS go‑to‑market teams (or equivalent high‑impact analytical experience).
- Demonstrated track record owning complex analytics projects end‑to‑end (from: scoping through production deployment and stakeholder adoption), ideally in forecasting, segmentation/territory design, or account/deal scoring domains.
- Experience working directly with senior commercial stakeholders (e.g.,: Sales/GTM leaders) and turning analysis into concrete decisions and measurable business impact.
- Advanced SQL and strong Python (or equivalent) for data wrangling, feature: engineering, and statistical / ML modeling.
- Comfort working with large, messy datasets spanning CRM (Salesforce), product: usage, and marketing/sales engagement tools.
- Experience with at least one modern BI / analytics environment (e.g., Sigma,: Looker, Tableau) and building self‑serve, governed assets for business users.
- Solid grounding in statistical thinking (sampling, backtesting, lift/impact: measurement) and model validation practices.
- Ability to decompose ambiguous business questions into structured analytical: plans, with clear assumptions, risks, and trade‑offs.
