Senior Data Scientist - Flex Pay
At a glance
Senior Data Science role at Upgrade. San Francisco · $100,000–$150,000 base.
dataroles summary, based on the employer's posting.
What you'll do
- Create and upkeep credit and fraud policy simulators to check system behavior and spot improvements to risk decisions
- Develop and launch statistical and machine-learning models for credit risk, fraud, pricing, customer groups and marketing attribution
- Test models for factors that could change their performance, then analyze structured and unstructured data with SQL and Python or similar tools
- Work with risk, marketing, product and engineering teams to shape data-led strategies
What you bring
- An advanced degree in a quantitative area such as data science, statistics or mathematics
- At least three years in data science or analytics, ideally in financial services
- Strong working ability with Python and SQL
Who this fits
This role suits a senior data scientist focused on predictive modeling, risk decisions, pricing and customer insights. It is intended to be hybrid from the San Francisco office, with remote applicants also considered. The stated annual salary range is $100,000 to $150,000, plus equity and benefits.
From the employer
About the Role:
We are seeking a highly analytical and results-driven Data Scientist to join our buy now pay later (BNPL) sector called Flex Pay. You will play a key role in building predictive risk models, optimizing offers and pricing, and extracting insights that drive product development, risk mitigation, and customer strategy. This role requires a strong foundation in statistics, machine learning, and programming.
While this position is ideally based in our San Francisco office on a hybrid basis, we are also open to considering remote candidates.
What You’ll Do:
- Build and maintain credit and fraud policy simulators used to ensure properly functioning systems and identify risk decisioning enhancements.
- Build and deploy statistical models and machine learning algorithms to solve business problems in areas like credit risk, fraud detection, pricing, customer segmentation, and marketing attribution.
- Validate models to identify factors that may affect model performance.
- Analyze large, structured and unstructured datasets using SQL, Python or similar tools.
- Stay up to date with the latest trends and technologies in data science and fintech, actively research new tools and techniques available for model development.
- Collaborate with cross-functional teams including risk, marketing, product, and engineering to define data-driven strategies.
What We Look For:
- Advanced Degree (MS/PhD) in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative discipline.
- 3-5+ years of hands-on experience in a data science or analytics role, preferably in financial services.
- Experience and/or strong interest in machine learning techniques (Random Forest, Gradient Boosted Trees, etc.) strongly preferred.
- Strong proficiency in Python (Pandas, Numpy, Scikit-learn) and SQL.
- Ability to write documentation and present analysis to people with different levels of expertise (e.g., technical staff, business leads, etc.).
- Proactive, driven, and ability to work in a fast paced environment.
Nice to Have:
- Experience with data visualization tools (e.g., Tableau, Power BI).
- Experience with AI tools such as Claude
- Experience with data technologies like PySpark.
- Understanding of financial services concepts such as credit scoring, portfolio risk, or customer lifetime value.
What We Offer You:
- Competitive salary and stock option plan
- Paid coverage of medical, dental and vision insurance
- Competitive 401(k) and RRSP program
- Flexible PTO
- Opportunities for professional growth and development
- Paid parental leave
- Health & wellness initiatives
The compensation range of this position in San Francisco, CA is USD $100,000-$150,000 annually plus equity and benefits. Within this range, an individual's base pay will be dependent on a variety of factors, including without limitation, job-related knowledge, skills, education, and experience.
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