Analyst II, Full Stack

Remote from: USA
Salary, yearly, USD: 124,000 - 190,000
Department: Programming
Employment type: Full Time,
Job posted: 14 May 2026
Apply before: 14 Jun 2026
Experience level: Senior
Views / Applies: 19 / 5

About company

About Affirm

Buy now. Pay later. Simple and transparent financing.

Financial Services
2012

Actively Hiring

AI Summary

Affirm is hiring a Full Stack Analyst II for its Operations Analytics team. The role involves forecasting, planning, and data analysis to support customer operations, with a focus on UK and new geography launches. The analyst will build and maintain forecasting models using time series, regression, and machine learning, and will integrate AI/LLM tools into the analytics workflow. The ideal candidate has a quantitative background, strong SQL and Python skills, and experience with cloud-native data platforms. This position offers a salary range of $124,000 to $190,000 per year.

Job Complexity

Easy Hard

AI Insight The role requires advanced technical skills (SQL, Python, AI/ML, forecasting) and the ability to operate independently in ambiguous environments, making it challenging. However, it does not require senior-level leadership or years of experience beyond 5+.

Salary Analysis

AI Insight The offered salary range of $124,000 to $190,000 is competitive for a mid-to-senior level analytics role. The median of $157,000 aligns well with market rates for similar positions in fintech and tech hubs, though the top end exceeds typical market max for this role level.

Key Skills

SQL Python Machine Learning Forecasting AI/LLM Data Analytics Cloud Platforms Stakeholder Management Optimization Automation

Cover Letter Sample

I am writing to express my strong interest in the Analyst II, Full Stack position at Affirm. With over five years of experience in quantitative analysis and forecasting, I have developed a robust skill set in SQL, Python, and machine learning that aligns perfectly with the requirements of this role. My background includes building and validating statistical forecasting models and integrating AI tools into analytics workflows, which I am eager to apply to support Affirm's operations planning.

In my previous role, I successfully owned demand forecasting for multiple regions, improving accuracy by 15% through the use of time series and regression models. I also collaborated with cross-functional teams to frame planning problems and drive data-informed decisions. I am particularly drawn to Affirm's mission of making credit more honest and friendly, and I am excited about the opportunity to contribute to the UK and new geography launches.

I thrive in ambiguous environments and have a strong bias toward automation, which I believe will enable me to build durable systems that enhance efficiency. I am confident that my technical expertise and stakeholder management skills will allow me to deliver high-impact insights and support Affirm's growth. Thank you for considering my application.

Possible Interview Questions

Can you describe a time when you built a forecasting model from scratch? What steps did you take to ensure its accuracy and reliability?

I built a time series forecasting model for call volume using historical data. I started by cleaning and exploring the data, then selected an ARIMA model after comparing it with other methods. I validated the model using backtesting and tracked error metrics like MAPE. I also implemented a monitoring system to detect drift and retrain the model periodically.

How have you integrated AI or LLM tools into your analytics workflow? Can you provide a specific example?

In a recent project, I used an LLM to generate initial SQL queries for data exploration. I then validated and refined those queries, and used the LLM to help write Python code for feature engineering. I also used AI to summarize findings for stakeholder presentations, ensuring the outputs were accurate and trustworthy.

Describe a situation where you had to work on an ambiguous problem without clear direction. How did you approach it?

I was tasked with optimizing agent scheduling but had no predefined metrics. I started by interviewing stakeholders to understand their pain points, then defined key performance indicators like utilization and service level. I built a simulation model to test different schedules and presented my findings, which led to a 10% improvement in efficiency.

How do you ensure your forecasting models are robust and avoid overfitting?

I use techniques like cross-validation, regularization, and out-of-sample testing. I also incorporate domain knowledge to limit model complexity and regularly backtest against historical data. I track performance metrics over time and retrain models as needed to adapt to changing patterns.

Can you explain a complex analytical concept to a non-technical stakeholder? How do you make it accessible?

I once explained a machine learning model's predictions to a marketing team by using a simple analogy: comparing it to a GPS that learns traffic patterns. I focused on the business impact—how it improved targeting—rather than technical details. I also used visualizations to show accuracy gains and answered questions patiently.

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.

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% subsidized medical coverage, dental and vision for you and your dependents.)
USA base pay range (CA, WA, NY, NJ, CT) per year: $140,000 – $190,000
USA base pay range (all other U.S. states) per year: $124,000 – 174,000
#LI-Remote

Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.

We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:

Health care coverage – Affirm covers all premiums for all levels of coverage for you and your dependents
Flexible Spending Wallets – generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
Time off – competitive vacation and holiday schedules allowing you to take time off to rest and recharge
ESPP – An employee stock purchase plan enabling you to buy shares of Affirm at a discount

We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.

[For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.

By clicking “Submit Application,” you acknowledge that you have read Affirm’s Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.

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