Senior Data Scientist, GTM Engineering

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Company Overview

A leading global fintech organization is redefining the future of financial services through a unified payments and financial platform designed for businesses worldwide. Supporting more than 250,000 businesses across multiple industries, the company provides integrated solutions spanning business accounts, payments, spend management, treasury services, and embedded finance. With a global workforce operating across numerous international offices, the organization continues to drive innovation in financial technology through scalable infrastructure, advanced software capabilities, and a commitment to building the next generation of global banking solutions.

The organization values individuals with founder-like energy who thrive in fast-paced environments and are motivated by ownership, continuous learning, and meaningful impact. Team members are encouraged to leverage emerging technologies, including AI, to solve complex challenges and deliver exceptional outcomes.

The GTM Data Science team is a collaborative group of analytics and data science professionals focused on driving commercial success through data-driven decision-making. Working closely with Product, Growth, and Commercial teams, the team develops scalable solutions that accelerate revenue growth, optimize operational efficiency, and establish strong data foundations for future business expansion.

The organization is seeking a Senior Data Scientist, GTM Engineering to play a pivotal role in addressing go-to-market challenges through advanced analytics and innovative data science solutions. The successful candidate will contribute across revenue forecasting, causal inference, and AI-driven insights throughout the commercial lifecycle while translating complex analytical findings into actionable business strategies.

Key Responsibilities

• Partner with Product, Growth, and Commercial teams to design and implement scalable data science solutions that improve revenue performance and commercial outcomes.
• Lead exploratory and data-driven analyses to identify revenue opportunities, uncover trends, and operationalize insights through automated and repeatable workflows.
• Develop and maintain revenue forecasting models and performance analytics, including pipeline health, conversion metrics, retention drivers, and scenario planning.
• Apply advanced causal inference techniques, including Difference-in-Differences (DiD), Synthetic Control, and DoubleML methodologies, to estimate business impact and support strategic decision-making.
• Design and implement AI-enabled solutions across the sales and customer lifecycle to improve sales effectiveness, customer retention, and expansion opportunities.
• Present technical findings and recommendations clearly to both technical and non-technical stakeholders, including executive leadership teams.
• Contribute to building next-generation data science capabilities and scalable analytics foundations across the organization.

Required Qualifications

• Minimum of 5 years of industry experience with an advanced degree (MS or PhD) in a quantitative discipline such as Statistics, Computer Science, Engineering, Economics, or a related field.
• Demonstrated analytical and structured problem-solving capabilities with experience translating commercial questions into actionable analytics initiatives.
• Excellent communication skills with the ability to present complex technical concepts to diverse audiences.
• Strong hands-on experience with SQL, Python, and/or R, including expertise in revenue forecasting and causal inference methodologies.
• Deep curiosity about go-to-market performance and customer behavior with a focus on understanding both outcomes and underlying drivers.
• Experience working with cloud data platforms or data warehouses such as Databricks is advantageous.
• Familiarity with notebook-based analytics tools, including Hex or similar platforms, is preferred.
• Experience in high-growth startup environments and/or B2B business models involving CRM, sales pipeline management, or Revenue Operations (RevOps) data is beneficial.

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