Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
Tailor your resume to this Senior Data Scientist - Credit and Lending ML role.
Rezi rewrites your resume against Mercury's job description. Free.
Tailor your resume to this Senior Data Scientist - Credit and Lending ML role.
Rezi rewrites your resume against Mercury's job description. Free.
Don't guess if your resume is good enough.
See how it scores against the Senior Data Scientist - Credit and Lending ML posting at Mercury — free, in seconds.
Don't guess if your resume is good enough.
See how it scores against the Senior Data Scientist - Credit and Lending ML posting at Mercury — free, in seconds.
About the Role
Mercury is building the future of financial infrastructure for startups and growing businesses by extending credit based on proprietary data. We are seeking Senior/Staff Data Scientists to develop and deploy machine learning models for credit decisions, partnering closely with Credit, Product, and Engineering teams.
Responsibilities
- Build and own the inference layer behind the small business lending program.
- Ship models into production with monitoring and documentation.
- Help Credit and Product teams determine lending strategies.
- Build and own models for transaction classification, recurring inflow/outflow detection, annual revenue estimation, forecasting with seasonality, and anomaly detection.
- Transform data streams 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, including data requirements and uncertainty quantification.
- Partner with Product on product features such as principal sizes, pricing, and repayment terms.
- Assess the impact of predictive modeling on the underwriting program.
- Set the technical standard for Machine Learning in Credit.
Requirements
- 3+ years of experience building ML models.
- At least 1 year of experience in lending or credit algorithms.
- Experience using deep-learning / transformer models in a professional context.
- Proficiency in Python and experience with production-grade services.
- Strong SQL skills and experience with data warehouses.
- Ability to explain models to diverse stakeholders (credit officers, model validators, PMs, engineers).
- Ability to set modeling standards for data science colleagues.
- A quantitative degree or equivalent experience.
Skills
- Machine Learning
- Python
- SQL
- Deep Learning
- Transformer Models
- Production Services
- Data Warehousing
- Underwriting Policy
- Predictive Modeling
- ML Operations
Location
- US employees (any location)
- Canadian employees (any location)
Work Type
- Full-time
Experience Level
- Senior/Staff
Education Level
- Quantitative degree or equivalent experience
Salary/Compensations
- US employees (any location): $166,600—$298,800 USD
- Canadian employees (any location): $157,400—$282,400 CAD
Benefits
- Base salary
- Equity (stock options/RSUs)
About the Company
- Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
- We are building the future of financial infrastructure for startups and growing businesses.
- We extend credit to businesses using proprietary access to data from bank transactions, financial reporting, and payroll.
Equal Opportunity
- 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.