Senior Data Scientist - Credit and Lending ML

Mercury; Canada; Presencial; CAD 157.000 - 282.000; Jornada completa

Descripción de la vacante

Mercury is hiring a Senior/Staff Data Scientist in Canada to build the inference layer for our small business lending program. You will turn diverse data into actionable health signals and deploy models with monitoring and tests, working closely with Credit, Product, and Engineering.

You'll-own models for transaction classification, revenue estimation, and forecasting, while shaping underwriting data needs and model standards for the team.

Formación

• 3+ years building ML models or similar experience.
• Exposure to deep-learning / transformer models in a professional context.
• Strong Python programming skills and production-grade services.
• Proficient SQL and familiarity with data warehouses.
• Ability to explain model decisions to non-technical stakeholders.
• Ability to set modeling standards for a team.
• Quantitative degree or equivalent.
• Experience in lending or credit algorithms is a plus.

Responsabilidades

• Build and own models for lending decisioning and health signals.
• Turn bank transactions and business data into defensible views of health.
• Deploy models to production with observability and tests.
• Collaborate with Credit on underwriting policies and data needs.
• Collaborate with Product on product shape, pricing, and terms.
• Assess where predictive modeling moves underwriting program.
• Set technical standards for ML across the team.

Conocimientos

ML modeling

Python

SQL

Data warehouse

Model explainability

Educación

Quantitative degree

Descripción del empleo

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 3+ years building ML models, and at least 1 year in lending or credit algorithms
• Be opinionated and thoughtful on where ML can best lift an underwriting program
• Some exposure to using deep-learning / transformer models in a professional context
• Be strong in Python, with experience working on production-grade services
• 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.

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 USD

Canadian employees (any location):

$157,400—$282,400 CAD