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Senior Director of Decision Analytics

Payactiv

Job Location:

Milpitas, CA - USA

Yearly Salary: USD 240000 - 260000

Posted: 29 September 2026 (Yesterday)

Application Deadline: 27 December 2026

Vacancies: 1 Vacancy

Job Summary

Job Title: Senior Director of Decision Analytics

Location: Payactivs Milpitas CA Headquarters

Reports to: Senior VP of Finance

Who we are

We are Payactiv a FinTech company in the heart of Silicon Valley devoted to giving workers access to their earned wages when they need them. Payactiv is the pioneer and industry leader in Earned Wage Access. We are the only Certified B Corporation and Public Benefit Corporation in our industry. Our app provides millions with financial services that help them avoid debt manage their finances and regain flexibility to pay for things on their own schedule.

As Payactiv expands its product offerings to serve workers throughout the U.S. the Decision Analytics team is seeking a Senior Director of Decision Analytics based out of our Milpitas office to further build out and lead the decision analytics function behind our product offerings. This is a high-visibility leadership role for a proven risk leader who has scaled risk analytics at a consumer fintech and is passionate about expanding financial access.

The Decision Analytics team is responsible for building the data-driven intelligence that powers many of Payactivs financial products and business decisions. We develop forecasting models decision engines risk strategies and other analytical solutions that help drive business performance at scale. Working closely with Product Engineering Operations Finance and other stakeholders we turn data into actionable insights and automated decisions that improve customer outcomes strengthen partner relationships and support long-term growth.

What you will do

Define and own the risk decision strategy for Payactivs product offerings partnering with executive leadership to strike the right balance between expanding access growth and loss performance.
Own end-to-end risk decisioning strategy: eligibility criteria advance limits repayment risk models debit and recovery strategies and portfolio management from initial design through deployment monitoring and iteration.
Build loss forecasting vintage and cohort analysis and unit-economics frameworks; own portfolio metrics such as approval rates loss rates repayment performance and risk-adjusted profitability and report on them to executive leadership.
Build lead and mentor a high-performing team of risk data scientists and analysts setting the bar for technical rigor speed and business impact.
Champion a test-and-learn culture design run and read experiments that expand access to more workers while holding losses within appetite.
Establish model governance standards documentation validation performance monitoring and controls that keep pace with regulatory expectations.
Act as a trusted advisor to Product Engineering Finance Compliance and executive stakeholders translating complex analyses into clear decisions; partner with fraud and identity teams where risk domains intersect.

What you need

Bachelors degree in a quantitative discipline Data Science Statistics Mathematics Economics Engineering etc.; advanced degree preferred.
8 years of experience in consumer credit risk risk analytics or decision science including 3 years building and leading analytical teams.
Direct experience owning risk at a consumer fintech earned wage access cash advance BNPL or near-prime consumer lending (e.g. EarnIn Dave Brigit MoneyLion Affirm Klarna) strongly preferred.
A proven track record of owning risk decision strategy and loss outcomes at scale including experience serving U.S. workers with deep fluency in loss forecasting portfolio management unit economics and experimentation and the ability to connect risk decisions directly to P&L outcomes.
Hands-on experience debiting consumer financial instruments at scale across methods ACH debits (standard and same-day) merchant-initiated debits of debit and prepaid cards via payment networks (e.g. Visa Mastercard) and emerging real-time rails (RTP FedNow) including debit timing sequencing and retry strategies and managing returns NSF chargebacks and disputes.
Hands-on technical depth in SQL and Python with working knowledge of modern ML approaches to risk modeling (e.g. gradient boosting frameworks) credible going deep with your team.
Executive-level communication skills able to distill complex risk trade-offs for C-suite and board audiences and influence decisions across functions.
Working knowledge of U.S. payment rails and the rules that govern consumer debits (e.g. Nacha operating rules Regulation E) and of the broader regulatory landscape for earned wage access and consumer credit is a strong plus.

What we offer