Credit Risk Analytics Manager

Mkiii; Reston (VA); On-site; USD 130,000 - 200,000; Full time

Benefits offered by this job

Equity

Quarterly bonuses

Medical

Holidays & time off

Remote work option

DC-area in-person option

Job summary

MarkIII is seeking a senior risk analytics professional to lead loss forecasting, credit policy, and fraud analytics for a fast-growing lender. You’ll work directly with the Chief Risk Officer and C-suite to shape strategy, governance, and data-driven decisions across the portfolio.

You'll own vintage analysis, CNL forecasting, and policy testing, using SQL, R, Python, and AI tools. The role offers equity, quarterly bonuses, and a flexible, mostly in-person/DC-area setup with remote options.

Qualifications

• 5+ years in credit risk, credit strategy, portfolio analytics, or fraud analytics at a lender, bank, fintech, or bureau.
• Deep consumer or small business loan performance analysis experience.
• Expert SQL, plus fluency in R or Python; comfort with the other language.
• Experience with credit bureau attributes and trended data is a plus.

Responsibilities

• Live in time-series loss forecasting: vintage curves, roll rates, transition matrices, delinquency migration, and recovery timing.
• Lead CNL forecasting by cohort, vintage, and segment with confidence intervals and macro stress scenarios.
• Decompose drivers: credit-band mix, channel, lead source, and term to isolate changes.
• Own the fraud/credit interaction and monitor rule performance; ensure explainability of decisions.

Skills

SQL

R

Python

AI tooling

Education

Quantitative degree

Tools

R & tidyverse

Premier Attributes

PowerCurve

Experian attributes

Production decisioning stack

Job description

MarkIII (MKIII) is a profitable, fast-growing company that specializes in off-the-shelf, insurance-backed lending programs. Our products provide lenders with capital relief and risk protection on portfolios originated on leading digital marketplaces (like Credit Karma), underwritten by MKIII's proprietary credit models.

MKIII succeeds by ensuring that all parties make money. We call it "The Formula," and The Formula works when we hit our risk targets — that's where you come in.

Why this role exists

The short version

MarkIII (MKIII) is a profitable, fast-growing company that specializes in off-the-shelf, insurance-backed lending programs. Our products provide lenders with capital relief and risk protection on portfolios originated on leading digital marketplaces (like Credit Karma), underwritten by MKIII's proprietary credit models.

MKIII succeeds by ensuring that all parties make money. We call it "The Formula," and The Formula works when we hit our risk targets — that's where you come in.

Why this role exists

Most lending risk functions are inherited rather than designed. Credit strategy lives in one team, fraud in another, and everything each side learns — the anecdotes, the pattern recognition, the hard-won rules of thumb — stays on its own side of the wall. So nobody can say what a loss is actually made of.

We don't carry the organizational debt that forces most lenders into that shape. What we do have is a real book with real history, and a growing team that mines it endlessly to keep our credit screens, verifications, and funnel tuned against "Risk" with a capital R — credit and fraud in one view — while making sure the best borrowers fund fast.

We want someone who has seen how this gets done at scale, has opinions about why parts of it are wrong, and wants to build the version they'd design from scratch.

Who you'll work with

You’ll report directly to the Chief Risk Officer, who is in this data daily — a real counterpart to pressure-test your conclusions, not a vacuum and not a rubber stamp. You’ll also work with the C-suite daily. Risk analytics here is a seat at the table, not a request queue. The function is growing, and you’ll be a senior voice in how it gets built and who joins it.

The scope below is the team's agenda, not a one-person to-do list.

The work Backbook Backbook performance and loss forecasting

• Live in the time series. Vintage-level loss curves, roll rates and transition matrices, delinquency migration, prepayment, recovery timing and severity — known well enough that an anomaly announces itself before it reaches a monthly report.
• Forecast cohorts to terminal loss. Lead CNL forecasting by cohort, vintage, and segment: the estimate, the confidence around it, what would have to be true for it to be wrong, and the macro stress cases around it.
• Decompose the drivers. When performance moves, isolate why — credit-band mix, channel and lead source, attribute shifts, sizing and term, seasoning, servicing, macro. Be clear whether you're looking at a mix shift or real credit deterioration.
• Feed the valuation. Your loss and timing curves drive cash-flow valuation of the portfolio and reserve adequacy. You're a contributor to that work, not a data supplier to it.

Policy Credit policy and buy-box strategy

• Find the expansion. Segment the reject population, size incremental volume against incremental loss, and bring us a swap-set analysis with an honest number attached.
• Size every policy change before it ships. Approval rate, volume, expected loss, blended cohort EL, and the effect on staying inside the insured target band.
• Help manage the box mid-cohort. As early vintages come in, recommend the tightening or loosening that keeps the blended cohort on target — then track whether it worked.
• Keep the policy documents true. Current, versioned, audit-ready: the rules as they actually run in production, the analysis behind each one, and the change history. Our lending partners and our carrier read these during diligence.
• Design the tests. Champion/challenger, holdouts, controlled expansions — measurement plan written before the test starts.

Fraud Fraud strategy and analytics

• Build the fraud performance view across both books: application fraud, synthetic identity and ID theft, first-party and never-pay behavior, income and employment misrepresentation, and on the SMB side business verification failures, straw owners, and shell entities.
• Separate fraud loss from credit loss. Own fraud tagging and attribution so each strategy is measured against the losses it actually causes. Get this wrong and both forecasts are quietly corrupted.
• Evaluate the vendor stack on our data, not their deck. Identity verification, device and behavioral signals, bureau fraud scores, KYB and business verification, income and bank-account verification. The case is incremental fraud caught against good applicants lost against cost per decision.
• Place controls in the funnel. Pre-qual, application, pre-funding, or post-funding monitoring — each carries a different cost, a different friction profile, and a different interaction with the credit rules already running there. Own that positioning.
• Own the fraud/credit interaction. No fraud declines silently absorbing credit risk; no credit cuts taking credit for fraud savings. Almost nobody does this well, and it's much of why this role exists.
• Monitor rule performance. Hit rates, false positives, verification pass rates, step-up conversion, rule overlap. Backtest on historical applications before go-live, and retire rules that have stopped earning their friction.

Governance Governance and communication

• Keep methodology, assumptions, data lineage, and results in audit-ready form. Our lending partners, our carrier, and their diligence teams read this work.
• Support model validation and program diligence.
• Turn a complex credit narrative into an executive read: the finding, the number, the confidence, the recommended action.

What we're looking for Required

• 5+ years in credit risk, credit strategy, portfolio analytics, or fraud analytics at a lender, bank, credit union, fintech, or bureau — with 3+ years hands-on, driving real portfolio or policy decisions.
• Real depth in consumer or small business loan performance analysis: vintage and static pool analysis, roll rates, loss curve fitting, CNL forecasting, segment-level attribution. Be ready to walk us through a loss forecast you built and defend the choices in it.
• Expert SQL, plus strong R or Python. We're an R shop for portfolio analytics and use Python where the platform requires it — fluency in one and working comfort in the other is ideal.
• Fluency with AI tooling, and judgment about where it stops. We're deliberately becoming an AI-forward shop; Claude is part of how we work daily, not a side experiment. We want someone who will push that further — and who knows which parts of a credit and fraud process have to stay explainable.
• Hands-on work with credit bureau attributes, including trended attributes.
• Experience contributing to written credit policy, underwriting criteria, or risk-appetite documentation.
• A genuine appetite for living inside historical performance data, the patience to mine it methodically, and the creativity to find the question nobody thought to ask.
• A point of view on what a modern risk strategy should look like, and the willingness to argue for it — including with us.
• The ability to explain a technical finding to a non-technical decision-maker without flattening it into something useless.

Nice to have

• Unsecured personal loans and/or small business lending experience.
• Fraud analytics — fraud strategy, rule development, identity verification, or vendor evaluation. Deep credit experience plus real fraud curiosity works too; we'll teach the rest.
• Model development — scorecards, gradient boosting, survival analysis, logistic regression. Useful, but this role is much more strategy, policy, and forecasting than model building.
• Marketplace or embedded distribution channels, and the adverse selection that comes with them.
• Experian attribute sets and decisioning tooling (Premier Attributes, PowerCurve, trended products), or the equivalent at another bureau.
• Insurance-wrapped or structured credit: expected loss targets, reserve and deductible mechanics, CNL triggers, cash flow valuation.
• ECOA/Reg B adverse action, fair lending testing, FCRA, or model governance practice — you'll work inside these, so knowing their shape helps.
• Fraud and identity vendors — Socure, SentiLink, Alloy, Persona, Sardine, Prove, Middesk, Enigma, or similar.
• A quantitative degree. Equivalent practical experience counts; we care what you've built, not where you studied.

We're not asking for an ML research background, an MLOps stack, or a distributed systems résumé. We want someone unusually good at reading a loan portfolio — who also wants to decide how it should be read.

Tools

R and the tidyverse, Python, SQL, Experian credit attributes including trended data, our production decisioning stack, and the marketplace integration layer.

On top of that, we're building an AI-forward analytics function on purpose. Claude and adjacent tooling are in the daily workflow — exploratory analysis, code, documentation drafts, tearing apart a fraud vendor's methodology before we buy it. Use them to move faster, and be rigorous about where they don't belong: a credit decision has to be explainable to a partner, a carrier, and a regulator, and "the model said so" is not a defense. If something that would make this work better isn't here yet, you'll have latitude to bring it in.

Compensation & benefits

Salary

Competitive and commensurate with experience, within the range of $130K–$200K. This is a good-faith estimate of what we expect to pay for this role at hire, and placement depends on depth of portfolio and policy experience and breadth across credit and fraud.

Equity

Company equity, so you benefit directly as the company grows.

Quarterly bonuses

Revenue-based bonuses paid quarterly.

Medical

100% of your medical premiums covered by the company, with the option to pay the difference to upgrade to the next tier.

Holidays

All federal holidays off plus additional company-wide days off.

Flexible time off

Sick leave as needed, and time off when you need or want it.

Location

• We'd like you in the Washington, DC area so we can work through this in person — whiteboards beat Zoom for portfolio work. For the right person, we'll go fully remote.