Staff Data Scientist - Credit and Lending ML
MercuryRemoteposted 8h ago
- PayUSD / yr
- $167k – 299k
- Where
- Remote (or Remote within Canada or US)
- Posted
- Nov 4
- 8 hours ago
- Type
- Full-time
- Setup
- Remote
At a glance
Own the ML inference layer for Mercury’s small-business lending program, turning financial data into trustworthy credit decisions. Partner with Credit, Product, and Engineering to deploy monitored models and set the technical bar for ML.
Summarized by AI from the original posting
What you'll do
- Build and own lending models for transaction classification, recurring inflow/outflow detection, revenue estimation, seasonal forecasting, and anomaly detection
- Transform bank transactions and other messy data streams into assessments of business health
- Deploy models to production with observability, monitoring, and tests
- Partner with Credit on underwriting policy and uncertainty quantification
- Partner with Product on lending product design, pricing, and repayment terms
- Assess where predictive modeling improves underwriting
- Set technical standards for ML in Credit
Requirements
- 017+ years building ML models
- 02Experience implementing models that shape automated financial decisions
- 03Production-grade Python
- 04Proficiency with deep learning, transformers, and LLMs
- 05Strong SQL and data warehouse experience
- 06Ability to explain models to technical and credit stakeholders
- 07Ability to set modeling standards
- 08Nice to have: bank transaction data, small business lending, business credit cards, revenue-based financing, or merchant cash advances experience
Perks
Skills
Full description
About a week before Hurricane Sandy made landfall, the two leading forecast models flatly disagreed: the American one carried the storm safely out into the Atlantic, the European one turned it hard left into New Jersey. The European model wasn't running a better simulation, it was running an ensemble. Hundreds of thousands of people were evacuated on the strength of the spread in forecast distribution, days before any single forecast could have justified moving them, and on October 29th, 2012, Sandy came ashore almost exactly where the models had put it.
At Mercury, we're building the future of financial infrastructure for startups and growing businesses. We're extending credit to those businesses, and we're doing it with proprietary access to some of the best data for understanding a borrower's business, from bank transactions to financial reporting to payroll. We're looking for a Senior/Staff Data Scientists to turn that record into a lending decision, partnering closely with Credit, Product, and Engineering.
In this role, you'll build and own the inference layer behind our small business lending program; you'll ship it into production with the monitoring and documentation that make it trustworthy; and you'll help Credit and Product decide what we should be lending in the first place.
Here Are Some Things You'll Do On The Job:
- Build and own the models behind our lending program - transaction classification, recurring inflow/outflow detection, annual revenue estimation, forecasting with seasonality, anomaly detection, and more
- Turn messy data streams - bank transactions, storefront sales, and more - into a defensible view of a business's health
- Deploy models into a production environment with observability, monitoring, and tests
- Partner with Credit on underwriting policy: what data is asked for, when follow-up information is worth the friction, and how to quantify uncertainty
- Partner with Product on the shape of the product: from principal sizes and pricing to repayment terms
- Assess where predictive modeling actually moves the underwriting program
- Set the technical bar for ML in Credit
You Should:
- Have 7+ years building ML models
- Implemented models that shaped automated decisions with money on the line
- Write production-grade Python
- Have depth beyond classification models; for example: time series and forecasting, anomaly detection, and NLP models
- Be proficient with deep learning, transformers, and LLMs
- Be strong in SQL and at home in a data warehouse
- Be able to explain a model to a credit officer, a model validator, a PM, and an engineer
- Be able to set modeling standards for your data-science colleagues
- Have a quantitative degree, or the equivalent built some other way
Nice To Have:
- Direct experience modeling on bank transaction data
- Experience in small business lending or business credit cards
- Experience with revenue-based financing, merchant cash advances, or similar products
*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
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Total Rewards
The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.
Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees (any location):$166,600—$298,800 USDCanadian employees (any location):$157,400—$282,400 CAD