Upgrade is a fintech company that provides affordable and responsible credit, mobile banking, and payment products to everyday consumers. We were the fastest growing company in the Americas last year according to the Financial Times and Upgrade Card was the fastest growing credit card in America two years in a row. We have delivered over $33 billion in affordable and responsible credit to our 5.5M customers. The company is backed by some of the most prominent technology investors and was recently valued at $6.3B.
We have built an energizing, collaborative and inclusive culture where team members help each other, learn and innovate to move the company and its customers in the right direction, and own the outcome of their efforts.
Upgrade has been named a “Best Place to Work in the Bay Area” three years in a row, “Top Companies to work for in Arizona” and one of the "Best Engineering Department" awarded annually by Comparably. We've also received recognition for being a best company for Diversity, Women, Culture, and Veterans.
We are looking for new team members who get excited about designing and delivering new and better products to join a team of 1850 talented and dedicated professionals. Come work with us if you like to tackle big problems and make a meaningful difference in people's lives.
About the Role
Upgrade’s Model Risk Management team plays a critical role in ensuring the integrity and reliability of the machine learning/statistical models used across the company. As a part of Upgrade’s risk function, we provide effective and independent challenges to assess, validate, and mitigate risks related to model development and usage, ensuring they align with regulatory standards and best practices. As a Model Risk Analyst, you will be integral in identifying model risks and strengthening model robustness and performance.
What You’ll Do:
Reporting to Upgrade’s Director of Model Risk Management, this role will:
- Review and validate statistical/machine learning models for various business areas, including Credit Risk, Fraud, Marketing, and Operations.
- Review and challenge model methodology, data process, outcome analysis to identify gaps and improve on model performance.
- Actively research new tools and techniques available for model development and validation.
- Collaborate with various teams across Upgrade in model validation process: Decision Science, Credit Risk, etc.
What We Look For:
- Advanced Degree (MS/PhD) in Statistics, Mathematics, Data Sciences or a related quantitative discipline.
- 0-2 years experience as a Data Scientist, Statistician, or related modeling background. Recently graduated students welcome to apply.
- Experience and/or strong interest in latest machine learning techniques (Random Forest, Gradient Boosted Trees, etc.) strongly preferred.
- Ability to go deep into complex technical topics, understand modeling and algorithms.
- Proficient in Python.
- Ability to write documentation and present analysis to people with different levels of expertise (e.g., technical staff, business leads, etc.).
- Detail oriented and strong analytical skill set.
- Proactive, driven, and ability to work in a fast-paced environment.
Nice to Have:
- Experience with SQL is a plus.
- Financial services background.
What We Offer You:
- Competitive salary and stock option plan
- 100% paid coverage of medical, dental and vision insurance
- Flexible PTO
- Opportunities for professional growth and development
- Paid parental leave
- Health & wellness initiatives
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Top Skills
What the Team is Saying
What We Do
Upgrade offers affordable responsible credit and payment products to everyday consumers. Our innovative financial products help our customers move their finances in the right direction and put more money in their pocket.
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Upgrade, Inc. Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
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