Role in brief
Mercury is a fintech company focused on building financial infrastructure for startups. The Senior Data Scientist will develop lending models and collaborate with various teams to enhance credit decisions. Candidates with strong ML, Python, and SQL skills, particularly in lending, should apply.
About the role
Mercury is dedicated to transforming financial infrastructure for startups and growing businesses, particularly through innovative credit solutions. As a Senior Data Scientist, you will be responsible for creating and managing models that enhance the lending program by leveraging diverse data sources, from bank transactions to financial reports.
In this role, you will work closely with Credit, Product, and Engineering teams to ensure that models are not only effective but also align with business needs. Success in this position involves not only technical proficiency in machine learning and data manipulation but also the ability to communicate complex concepts clearly to various stakeholders.
The salary range reflects competitive compensation within the SaaS and fintech sectors.
Skills that matter here
- ML: You will build and own machine learning models for the lending program.
- Python: Strong Python skills are required for developing production-grade services.
- SQL: Proficiency in SQL is essential for working with data warehouses and managing data streams.
Who this role suits
- A candidate with a solid background in machine learning and experience in lending or credit algorithms.
- Someone who can communicate complex technical details to non-technical stakeholders.
- A person who is proactive and has strong opinions on the application of ML in underwriting.
- An individual who values collaboration and can set high standards for data science practices.
From the employer
Responsibilities
- 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.
Requirements
- 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.
Conditions
- The total rewards package includes base salary, equity (stock options/RSUs), and benefits.
- Competitive salary and equity ranges within the SaaS and fintech industry.
- New hire offers are based on experience, expertise, geographic location, and internal pay equity relative to peers.
- Target new hire base salary ranges for this role are:
- US employees (any location): $166,600 — $298,800 USD
- Canadian employees (any location): $157,400 — $282,400 CAD.
Questions about this role
Is this role remote?
Yes, this position is fully remote.
What is the salary range for this position?
The base salary for this role ranges from $166,600 to $298,800 USD for US employees.
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