Job Description

About the Role:

We are seeking a highly analytical and results-driven Data Scientist to join our growing analytics team in the financial services sector. You will play a key role in building predictive models, optimizing financial decisions, and extracting insights that drive product development, risk mitigation, and customer strategy. This role requires a strong foundation in statistics, machine learning, and financial domain knowledge. This position is based in our San Francisco office in a hybrid capacity, specifically on Tuesdays, Wednesdays, and Thursdays.

What You’ll Do:

• 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.
• 0-2 years of hands-on experience in a data science or analytics role, preferably in financial services.
• Experience and/or strong interest in the latest 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.).
• Detail oriented and strong analytical skill set.
• Proactive, driven, and ability to work in a fast paced environment.

Nice to Have:

• Experience working with cloud platforms (AWS, GCP, or Azure).
• Experience with data visualization tools (e.g., Tableau, Power BI).
• Knowledge of big data technologies like Spark or Hadoop.
• Understanding of financial services concepts such as credit scoring, portfolio risk, or customer lifetime value.

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