Senior GTM Data Scientist

About the role

As a , you will be a critical analytical partner to our Go-To-Market (GTM) teams. You will embed yourself in our GTM data to uncover insights and drive actionable recommendations across Sales, Marketing, and Customer Success. The core of this role is to design, build, and maintain predictive machine learning models that optimize customer acquisition, revenue attribution, and retention efforts. You will apply analytical rigor and methodologies like experimentation and causal inference to provide GTM leadership with a reliable understanding of business efficiency and impact. You will report to the Director of GTM Data and act as a reliable thought partner to Marketing, Sales, Customer Success, and Finance.

What you'll do

Predictive Modeling & GTM Strategy

You will design, build, and deploy foundational GTM models that directly influence business performance. This includes developing Customer Lifetime Value (LTV) forecasts to understand long-term revenue potential, creating Marketing and Sales Attribution models to identify the true impact of channels and touchpoints, and building Propensity models to predict behaviors such as conversion likelihood, churn risk, and expansion potential. These models will serve as the foundation for strategic decision-making and resource allocation across the organization.

GTM Experimentation

You will partner closely with GTM teams to design and analyze controlled experiments that test hypotheses and drive optimization. You will run website A/B tests, evaluate pricing experiments, and assess marketing campaign effectiveness using robust methodologies. Your expertise will include A/B testing, multivariate testing, Bayesian approaches, and Causal Inference to ensure results are valid, reliable, and actionable.

Deep Dive Analysis

You will execute proactive, complex analytical deep dives to uncover latent user behavior and identify root causes behind changes in key GTM metrics. By exploring data systematically, you will translate findings into clear, actionable recommendations that guide strategy and execution at scale.

Marketing Mix Modeling (MMM)

You will support the interpretation of Marketing Mix Modeling results to maximize marketing ROI. You will assess the feasibility of future in-house modeling, ensuring that insights derived from MMM align with business goals and data capabilities.

Measurement Frameworks

You will define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps GTM activities such as CAC, funnel conversion, and retention to high-level business outcomes. This framework will ensure consistency and clarity in how performance is measured and communicated.

Data Advocacy

You will translate complex statistical findings and model outputs into compelling business narratives. By crafting clear, data-driven stories, you will enable cross-functional partners to understand implications, make informed decisions, and act on insights confidently.

Data Partnership

You will work closely with Data Engineering to ensure data quality, reliable instrumentation, and the development of reusable predictive assets. This includes contributing to model feature stores and other infrastructure that supports scalable, maintainable models.

Guidance

You will provide technical guidance to peers and stakeholders on best practices for data exploration, ML modeling, and causal methodologies. Your expertise will help elevate the analytical maturity of the entire GTM organization.

Requirements

Experience

You bring 4+ years of professional experience in an applied data science, economics, or GTM analytics role. You have a proven track record of leveraging predictive modeling and experimentation to drive measurable business impact in real-world environments.

Education

You hold a B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline. A Master's degree is preferred but not required.

Technical Expertise

You demonstrate hands-on experience building and validating production-ready models for business applications, including LTV, attribution, and propensity. You apply practical Causal Inference methods such as Quasi-Experimentation, Matching Methods like PSM, and Difference-in-Differences. You are proficient in statistical methodologies for A/B testing, including sample size calculations, sequential testing, and variance reduction techniques. Your programming skills include advanced proficiency in Python or R, with specific experience in Scikit-Learn, pandas, and numpy, along with expert-level SQL. You have experience with data pipelining and tooling such as dbt, Airflow, Databricks, or Snowflake.

Key Attributes

You possess strong data storytelling skills and the ability to influence cross-functional stakeholders through clear, data-driven narratives and executive-level presentations. You are highly organized, with the ability to manage multiple projects and competing priorities in a fast-paced environment while maintaining a high degree of accuracy and ownership. You work comfortably both autonomously and collaboratively across distributed teams, committed to maintaining data integrity and fostering a culture of evidence-based decision-making.

Nice to have

The source text does not specify any preferred or "nice-to-have" qualifications beyond those listed in the requirements and technical expertise sections.

Practical notes

No information regarding hours, travel, or visa requirements is provided in the source text. The role is listed as Remote (USA) in location. All responsibilities, requirements, and attributes are derived strictly from the provided source content.