Staff Applied AI Scientist, Decision Systems

Headquartered in San Diego, Mulligan Funding serves as a leading provider of working capital (Up to $5M) to the small and medium-sized businesses that fuel our country. Since 2008, we have prided ourselves on our collaborative, innovative, and customer-focused approach. Enjoying a period of unprecedented growth, driven by the combination of cutting-edge technology, human touch, and unwavering integrity, we are looking to add to our people first culture, with highly motivated and results-oriented professionals, to push the limits of what’s possible while creating value for all of our partners.
As our Staff Applied AI Scientist focused on Decision Systems, you will design the logic behind how AI makes decisions inside real production workflows. This is not a research role and it is not a pure engineering role. You will sit at the intersection of business strategy, risk, and applied AI to ensure our decision systems are intelligent, calibrated, and economically aligned.
Engineers will deploy the systems. You will define the decision intelligence that powers them. If you are excited about turning business judgment into structured, production ready AI logic that directly impacts profitability, this role is for you.

What You Will Work On

• Designing and formalizing structured AI reasoning frameworks that translate credit policy, risk strategy, and operational heuristics into production ready decision logic.
• Defining multi layer decision hierarchies across underwriting, collections, fraud detection, pricing optimization, and sales routing workflows, including conflict resolution logic when signals disagree.
• Establishing structured output standards for agent based systems so AI recommendations are interpretable, consistent, and actionable.
• Designing confidence scoring methodologies for AI assisted decisions and calibrating routing thresholds across auto approve, escalation, human review, and decline paths.
• Optimizing trade offs across risk exposure, approval rates, speed to decision, unit economics, and operational capacity.
• Analyzing override behavior and feedback loops to refine decision logic and improve system performance over time.
• Building evaluation datasets to test reasoning quality prior to deployment and defining clear production acceptance criteria.
• Benchmarking performance across decision domains and establishing monitoring standards to detect drift, degradation, bias, or inconsistency in AI outputs.
• Partnering with Engineering and MLOps to ensure monitoring, reporting, and feedback mechanisms are embedded in production systems.
• Supporting governance, audit, and compliance documentation to ensure decisions are explainable and defensible in a regulated environment.
• Assessing potential bias and unintended impact across workflows and partnering with Risk, Compliance, and Legal on responsible deployment.
• Quantifying business impact across loss rates, recovery performance, operational cost per file, funnel conversion, and cycle time, and aligning decision systems with portfolio economics and strategic objectives.

What This Role Is Not

• Ownership of API development, integration pipelines, or infrastructure.
• Primary responsibility for MLOps tooling or production orchestration code.
• An academic or purely research focused AI position.
• A general program management or governance only role disconnected from production impact.

What You Bring

• Eight or more years of experience in applied data science, including at least three years working on AI enabled product systems. Experience in AI product systems is required.
• Demonstrated experience designing, calibrating, and validating production decision systems.
• Experience translating domain expertise into structured logic frameworks that can be deployed in operational environments.
• Strong foundation in statistics, probability, and decision theory.
• Experience evaluating and tuning model outputs, including large language model reasoning outputs.
• Experience working in regulated or risk sensitive environments preferred. Background in fintech, lending, financial services, or other high consequence decision environments strongly preferred.
• Master’s degree in Statistics, Applied Mathematics, Data Science, Economics, Computer Science, Engineering, or related quantitative discipline required. PhD preferred.

Why Join The Team

• This role offers the opportunity to shape how AI makes decisions across an entire lending platform.
• The work directly influences portfolio performance, unit economics, and operational strategy.
• You will operate in a company that is intentionally building AI into its core workflows, not experimenting at the margins.
• The syste