Analyst II, Quantitative Modeling & Forecasting

We're looking for intelligent, driven professionals to join our team. The Operations Analytics team leverages data-driven insights 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.

A significant component of this role involves leading the transition from a legacy servicing cost model to a scalable, AI-integrated architecture. The existing cost attribution model is a heuristic framework that synthesizes a complex array of operational and financial inputs and ultimately feeds into lifetime value modeling used to inform strategic decisions. This role will own that evolution end-to-end. The first 6–12 months will focus heavily on reverse-engineering the current model, developing a deep understanding of its inputs, logic, and business dependencies, defining the future-state design, and delivering a production-ready replacement that scales with the business. This is not a maintenance role: it requires architectural decision-making, strong stakeholder alignment, and direct integration of AI/ML capabilities into core financial decision systems. The ability to operate independently in ambiguous environments is essential.

What You’ll Do

• • Own the existing servicing cost attribution model end-to-end: master its inputs, logic, and business impact, then architect and deliver an AI-integrated replacement.
  • Design, develop, and maintain forecasting models using statistical techniques such as time series, regression, and machine learning to support operational contact forecasting, headcount planning, license forecasting, and budget planning.
  • Partner with internal stakeholders to frame planning and forecasting problems, develop supporting metrics and diagnostics, and enable high-quality decision-making.
  • Highlight opportunities for productivity and efficiency improvements through data insights from a budget and financial performance perspective.
  • Present analytical recommendations to leadership, drive timely decisions, and ensure clear and concise communication with cross-functional stakeholders.
  • Partner with operational planning and other analytical teams to understand the business context around headcount and workforce scheduling, and obtain the data needed to generate accurate forecasts.
  • Maintain a strong understanding of our evolving business and ever-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).
  • 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.