Senior Data Scientist, Finance Data Science

Finance Data Science turns complex financial and customer data into trusted decisions, scalable operating processes, and growth opportunities. We partner with GBSG Finance across strategy, embedded FP&A support, external reporting, GBSG LTV, and the foundational customer, subscription, and revenue data that supports the business.

As a Senior Staff Analyst, you will lead the hardest, most ambiguous analytical problems our business faces. You will work closely with GBSG FP&A, Finance Strategy, Product, Engineering, Data Platform, Accounting, and Investor Relations. The role combines financial acumen, data science, business judgment, and executive communication. You will frame the question before analysis begins, establish the metrics and causal levers that matter, and drive the work through to decisions that shape strategy, planning, reporting, and operating processes.

You will also help build the data products, evaluation systems, and AI-enabled workflows that make high-quality analysis more reliable and scalable across Finance.

• Frame and lead cross-team analytical problems involving growth, retention, ARPC, attach, LTV, pricing, portfolio decisions, forecasting, close and reconciliation, and external reporting.
• Define the metrics, business logic, and causal levers that leaders use to manage GBSG performance.
• Own the shared business context, definitions, and analytical reasoning that Finance agents and self-service tools rely on.
• Partner with Finance and business leaders to translate complex data into recommendations, investment choices, operating changes, and executive narratives.
• Build and own shared data products, semantic models, pipelines, and decision systems that multiple teams depend on. Partner with Engineering and Data Platform to harden them for long‑term use.
• Establish quality standards, monitoring, governance, and lifecycle ownership for the data products and models that support financial and customer decisions.
• Apply causal inference, experiments, quasi‑experiments, and synthetic tests to resolve contested attribution questions and guide decisions where no clean answer exists.
• Turn high‑value analytical bottlenecks into scalable AI and agentic workflows by defining quality standards, building evaluation frameworks, and setting appropriate delegation guardrails.
• Shape how Finance uses AI by sizing opportunities against business outcomes, defining what good looks like, and proving which analytical work can be delegated safely.
• Build the evaluation, monitoring, and governance practices that allow teams to use AI‑enabled analytical systems with confidence.
• Raise the analytical bar across the team through technical leadership, coaching, reusable methods, and clear standards for decision‑quality analysis.

• 10+ years of relevant experience in data science, analytics, finance, strategy, or a related field, with a track record of influencing important business decisions.
• Strong business judgment and financial acumen, with the ability to connect customer, subscription, revenue, and financial data to business outcomes.
• Advanced SQL and programming skills in Python, R, or a comparable language, along with experience working with large‑scale data platforms.
• Deep understanding of statistical reasoning, causal inference, experimentation, predictive modeling, and the limitations of each method.
• Experience leading ambiguous, cross‑functional problems where the question, metric definitions, or appropriate methodology must be established before analysis can begin.
• Experience designing and owning data products, analytical models, pipelines, or decision systems through their lifecycle.
• Experience evaluating AI‑enabled or non‑deterministic systems using approaches such as golden…