Principal GTM Operations Analyst

MongoDB•Austin, TX

1d•$84,000 - $165,000•Remote

About The Position

The Principal Analyst on MongoDB’s GTM Intelligence – Applied Science team sets technical direction for the GTM model portfolio and leads high-stakes analytics programs. The role spans problem framing with senior stakeholders, data engineering and model design, production workflows and dashboards, and executive-ready recommendations that shape how the sales organization operates. It is remote in the United States and supports stakeholders across global regions and GTM functions. The role owns one or more GTM model portfolio domains and their multi-quarter roadmaps, including decisions about what to build, retire, or invest in based on measured business impact. It partners with GTM Operations, Sales Strategy & Planning, Sales Leadership, and Central Analytics to develop analytical plans and recommendations for topics such as segmentation, territory design, account prioritization, pipeline health, and forecasting. The Principal Analyst develops and maintains statistical and machine-learning models, engineers data pipelines and features, and leads flagship initiatives such as Forecasting 3.0 / Atlas Forecasting Calculator, Customer 360 Revamp, GTM Metrics 3.0, and territory optimization. The role also builds governed self-service analytics assets, evaluates business impact through experiments and causal analysis, and communicates findings through executive-ready narratives and stakeholder working sessions. The position advances GTM data and AI capabilities by identifying uses for new data sources and setting standards for model testing, monitoring, documentation, and change management. It also mentors analysts and data scientists, develops documentation and training, and contributes to a culture of technical rigor and knowledge sharing.

Requirements

Approximately 8–12+ years of experience in analytics, sales operations, data science, or strategy/consulting supporting B2B or SaaS go-to-market teams, or equivalent high-impact analytical experience.
Track record of owning complex analytics projects end to end, from scoping through production deployment and stakeholder adoption; forecasting, segmentation/territory design, or account/deal scoring experience is ideal.
Experience leading multi-quarter, cross-functional analytics programs and influencing executive decisions without direct authority.
Experience setting technical direction or standards for a team or portfolio of production models.
Experience working directly with senior commercial stakeholders and turning analysis into concrete decisions and measurable business impact.
Advanced SQL and strong Python, or equivalent, for data wrangling, feature engineering, and statistical or machine-learning modeling.
Comfort working with large, messy datasets spanning CRM (Salesforce), product usage, and marketing or sales engagement tools.
Experience with at least one modern BI or analytics environment, such as Sigma, Looker, or Tableau, and building governed self-service assets for business users.
Solid grounding in statistical thinking, including sampling, backtesting, lift/impact measurement, and model validation practices.
Experience with experimentation and causal inference, such as A/B and holdout design, difference-in-differences, or regression discontinuity.
Experience with forecasting and time-series models.
Ability to decompose ambiguous business questions into structured analytical plans with clear assumptions, risks, and trade-offs.
Strong written and verbal communication skills, including explaining complex models and data decisions in clear, GTM-friendly language and building trust with non-technical stakeholders.
Bias toward action and ownership, including comfort making recommendations under uncertainty and iterating as new data arrives.

Nice To Haves

Prior Sales Operations, Revenue Operations, or GTM Strategy experience at a high-growth SaaS company.
Experience with commercial applications of NLP or LLMs, such as call transcript analytics or opportunity notes analysis.
Familiarity with MongoDB’s ecosystem, including Atlas, GTM motions, and sales process, or similar modern cloud data platforms.
Experience contributing to Customer 360, self-service analytics, or GTM data and metrics standardization programs.

Responsibilities

Own the model portfolio and multi-quarter roadmap for one or more GTM domains, deciding what to build, retire, or invest in based on measured business impact.
Advise GTM leadership on analytical priorities and help shape the questions the team addresses.
Partner with GTM Operations, Sales Strategy & Planning, Sales Leadership, and Central Analytics to scope problems, define success criteria, and prioritize analytical work.
Structure ambiguous questions into hypotheses, analytical plans, and recommendations for senior stakeholders.
Develop and maintain statistical and machine-learning models that inform forecasting, territory assignments, and deal prioritization.
Build robust data pipelines and features using SQL and Python with data and platform teams.
Lead flagship programs including Forecasting 3.0 / Atlas Forecasting Calculator, Customer 360 Revamp, GTM Metrics 3.0, and territory optimization initiatives.
Build and govern self-service analytics assets, including Sigma workbooks and curated datasets.
Design experiments, holdouts, and causal analyses to quantify revenue and efficiency impacts of models and GTM investments.
Deliver executive-ready narratives, memos, scorecards, and QBR/MBR content with explicit recommendations and trade-offs.
Run stakeholder working sessions to align on scenarios, validate model outcomes, and incorporate recommendations into operating rhythms.
Define applications for new GTM data sources, including call transcript modeling, workload inference, and account intelligence.
Set standards for testing, monitoring, documentation, and change management of GTM models and analytics products with Central Analytics and GTM Tech.
Mentor analysts and data scientists, review technical work, and promote analytical rigor.
Create documentation, playbooks, and training to help analysts and GTM stakeholders understand and use models, tools, and datasets safely.
Promote technical rigor, peer review, and knowledge sharing within Sales Intelligence and the broader GTM Operations community.