About this role
Analytical professionals who thrive in financial technology will find a strong fit within the Flex Pay buy now pay later sector at Upgrade. The team focuses on building predictive risk models, optimizing pricing structures, and uncovering insights that directly shape product development and customer strategy.
Daily responsibilities center on developing credit and fraud policy simulators while deploying statistical algorithms for risk mitigation, segmentation, and marketing attribution. Success in this position requires a solid grounding in quantitative methods, programming, and cross-functional collaboration with engineering and risk departments.
Operating from the San Francisco office under a hybrid model on Wednesdays and Thursdays, the group tackles complex financial datasets to drive business growth. Team members enjoy an environment that encourages ongoing research into modern data tools and fintech trends.
What you will do
- Develop and maintain credit and fraud policy simulators to verify system functionality and spot risk decisioning improvements.
- Deploy statistical models and machine learning algorithms for credit risk, fraud detection, pricing, and customer segmentation.
- Validate predictive models to uncover variables that could influence overall performance.
- Examine large structured and unstructured datasets utilizing SQL and Python.
- Research new techniques and tools for model development within the fintech and data science space.
- Partner with risk, marketing, product, and engineering teams to establish data-driven strategies.
What you need
- Hold an advanced degree such as an MS or PhD in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative field.
- Bring three to five or more years of practical experience in analytics or data science, preferably within financial services.
- Demonstrate experience or a strong interest in machine learning methods including Random Forest and Gradient Boosted Trees.
- Show proficiency in SQL and Python libraries like Pandas, Numpy, and Scikit-learn.
- Communicate complex analysis effectively through clear documentation and presentations for technical and business audiences.
- Maintain a proactive and driven approach within a fast-paced setting.
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.
Benefits
- 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
Skills and tech stack
Questions candidates ask
Where is this job located and what is the remote policy?
This position is based in the San Francisco office in a hybrid capacity, specifically on Wednesdays and Thursdays.
What is the compensation range for the role?
The compensation range of this position in San Francisco, CA is USD $100,000-$150,000 annually plus equity and benefits. An individual's base pay depends on factors including job-related knowledge, skills, education, and experience.
What programming languages and tools are used on the team?
The team analyzes large datasets using SQL, Python, and similar tools, with specific proficiency required in Pandas, Numpy, and Scikit-learn. Additional exposure to Tableau, Power BI, Claude, and PySpark may also be utilized.
Which departments does this data science team collaborate with?
Team members collaborate with cross-functional groups including risk, marketing, product, and engineering to define data-driven strategies.