Description
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.
As a Senior Analyst on the Applied Science team, you will own high-impact analytical workstreams end-to-end: from problem framing with senior GTM stakeholders, to data engineering and model design, through to productionalized workflows, dashboards, and executive-ready narratives that drive concrete changes in the field.
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.
- Own core GTM analytics assets
- Contribute to flagship programs such as Forecasting 3.0 / Atlas Forecasting Calculator, Customer 360 Revamp, GTM Metrics 3.0, and territory optimization initiatives, ensuring they are statistically sound, explainable, and operationally durable.
- Build and govern self-serve analytics assets (e.g., Sigma workbooks, curated datasets) that allow GTM stakeholders to answer their own questions safely and consistently.
- Turn analysis into decisions and change
- Deliver concise, executive-ready narratives (memos, scorecards, and QBR/MBR content) that highlight trade-offs and explicit recommendations, not just data and charts.
- Run stakeholder working sessions to align on scenarios, validate model outcomes, and embed recommendations into operating rhythms (e.g., segmentation reviews, annual planning, forecast calls).
- Advance our GTM data & AI foundations
- Help shape and exploit new GTM data sources such as call transcript modeling, workload inference, and account intelligence to unlock novel scoring and routing use cases.
- Partner with Central Analytics and GTM Tech on best practices for testing, monitoring, documentation, and change management for GTM models and analytics products.
- Coach, document, and uplevel the org
- Create clear documentation, playbooks, and training to help other analysts and GTM stakeholders understand and safely use the models, tools, and datasets you own.
- Contribute to a strong culture of technical rigor, peer review, and knowledge sharing within Sales Intelligence and the broader GTM Operations community.
What We’re Looking For
Experience
- ~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.
Technical Skills
- 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.
Problem-Solving & Communication
- Ability to decompose ambiguous business questions into structured analytical plans, with clear assumptions, risks, and trade-offs.
- Strong written and verbal communication: you can explain complex models and data decisions in simple, GTM-friendly language and build trust with non-technical stakeholders.
- Bias toward action and ownership: you are comfortable making recommendations under uncertainty and iterating as new data arrives.