Senior Data Scientist - Flex Pay

Upgrade; San Francisco (CA); Hybrid; USD 100,000 - 150,000; Full time

Benefits offered by this job

Competitive salary and stock options

Medical, dental and vision insurance

401(k) and RRSP program

Flexible PTO

Parental leave and wellness programs

Job summary

Upgrade in San Francisco seeks a data scientist to build predictive risk models and optimize offers and pricing for its Flex Pay BNPL product. You’ll extract actionable insights for product development, risk mitigation, and customer strategy.

Hybrid role with in-office Wednesdays and Thursdays, collaborating with risk, marketing, product, and engineering. Strong ML, statistics, and Python/SQL skills required.

Qualifications

• Advanced degree in a quantitative field.
• Hands-on data science experience in financial services preferred.
• Proficiency in Python and SQL with model development exposure.

Responsibilities

• Build and deploy predictive risk models and ML algorithms.
• Validate models and analyze performance factors.
• Work with cross-functional teams to define data-driven strategies.
• Analyze large datasets using SQL, Python, or similar tools.

Skills

Python

SQL

Data analysis

Communication

Documentation

Team collaboration

Education

MS/PhD in Data Science or related

Tools

Pandas

NumPy

Scikit-learn

PySpark

Tableau

Power BI

Job description

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.

This position is based in our San Francisco office in a hybrid capacity, specifically on Wednesdays and Thursdays.

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.