Analyst II, Full Stack (Customer Servicing Analytics)

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. The Risk & Analytics team makes crucial decisions that direct Affirm’s business strategy. Our team designs and runs experiments to decide which product features to launch and which marketing campaigns to fund. We analyze performance not just to report on it, but to determine where to invest resources for maximum impact through building tools and analytical frameworks. We take ownership of the entire analytical lifecycle, from initial question to final business decision, ensuring Affirm grows efficiently and intelligently. We're looking for intelligent, driven professionals to join our team. The Operations Analytics team uses data to shape servicing strategy across contact channels, customer segmentation, capacity planning, forecasting, and repayment operations. The ideal candidate brings strong analytical depth, systems thinking, and the ability to partner cross-functionally to drive meaningful business impact. AI is integral to the analytics workflow on this team. The analyst applies AI from data exploration through model validation to stakeholder communication, and helps shape how the team uses it. The role requires architectural decisions, strong stakeholder alignment, and embedding AI/ML in core decisions. Operating independently in ambiguous environments is essential. What You'll Do Scope - Planning: Own planning and staffing for UK and new-geography launches and special teams. Develop and maintain the guardrail metrics. - Foundations: Build working familiarity with the underlying data, including demand signals, agent rosters (team, title, country), and agent activity (delivered hours, status), and license demand. - Infrastructure forecasts: Own demand forecasts for checkouts and app opens in CA and UK and new geographies. - Insights: - Report on forecast health for UK, CA, and other geos (accuracy and outlook). - Advise Customer Operations real-time management (UK) on agent productivity. - Curate AI knowledge for Customer Operations (UK) and UK Infrastructure forecasting. Responsibilities - Build and maintain forecasting models, using time series, regression, and machine learning, to support contact forecasting, headcount and license planning, and budgeting. - Partner with stakeholders to frame planning and forecasting problems, develop supporting metrics and diagnostics, and support high-quality decisions. - Surface productivity and efficiency improvements using budget and financial performance data. - Present recommendations to leadership, drive timely decisions, and communicate clearly with cross-functional partners. - Partner with operational planning and other analytics teams to understand the business context for headcount and workforce scheduling, and get the data needed for accurate forecasts. - Stay current on the business and our changing technical environment. What We Look For - Bachelor's, Master's, or PhD in a quantitative field (e.g., statistics, industrial engineering, operations research) and 5+ years solving forecasting, planning, or related quantitative problems. - Strong proficiency in SQL and Python or R, and hands-on experience with a modern cloud-native data platform (e.g., Databricks, Snowflake, BigQuery, or equivalent). - Hands-on experience using AI/LLM tooling across the analytics workflow: writing, validating, and iterating on AI-generated SQL and Python, and integrating AI outputs into analyses that stakeholders trust. - Experience building optimization models using linear programming techniques (e.g., CPLEX, Gurobi) is a plus. - Strong experience developing and validating statistical forecasting models, with disciplined approaches to performance measurement, backtesting, and robust error tracking. - Proven ability to independently structure ambiguous problems and select the appropriate analytical approach without predefined direction. - Clear, persuasive communicator with strong stakeholder management skills and the ability to influence senior leaders across technical and non-technical audiences. - Strong bias toward automation: treat repetitive manual work as avoidable drudgery and build durable, automated systems, delegating to machines what they can do more reliably and efficiently than humans. - High standards of humility, honesty, and ownership: you take responsibility for outcomes, invest in your own growth, and actively develop others. Pay Grade - K Equity Grade - 6 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100%

Bachelor's, Master's, or PhD in a quantitative field (e.g., statistics, industrial engineering, operations research) and 5+ years solving forecasting, planning, or related quantitative problems. Strong proficiency in SQL and Python or R, and hands-on experience with a modern cloud-native data platform (e.g., Databricks, Snowflake, BigQuery, or equivalent). Hands-on experience using AI/LLM tooling across the analytics workflow: writing, validating, and iterating on AI-generated SQL and Python, and integrating AI outputs into analyses that stakeholders trust. Experience building optimization models using linear programming techniques (e.g., CPLEX, Gurobi) is a plus. Strong experience developing and validating statistical forecasting models, with disciplined approaches to performance measurement, backtesting, and robust error tracking. Proven ability to independently structure ambiguous problems and select the appropriate analytical approach without predefined direction. Clear, persuasive communicator with strong stakeholder management skills and the ability to influence senior leaders across technical and non-technical audiences. Strong bias toward automation: treat repetitive manual work as avoidable drudgery and build durable, automated systems, delegating to machines what they can do more reliably and efficiently than humans. High standards of humility, honesty, and ownership: you take responsibility for outcomes, invest in your own growth, and actively develop others.

Build and maintain forecasting models, using time series, regression, and machine learning, to support contact forecasting, headcount and license planning, and budgeting. Partner with stakeholders to frame planning and forecasting problems, develop supporting metrics and diagnostics, and support high-quality decisions. Surface productivity and efficiency improvements using budget and financial performance data. Present recommendations to leadership, drive timely decisions, and communicate clearly with cross-functional partners. Partner with operational planning and other analytics teams to understand the business context for headcount and workforce scheduling, and get the data needed for accurate forecasts. Stay current on the business and our changing technical environment.