Quantitative Analytics Engineer

Pay range: USD $145,000.00 - $200,000.00 / Year

Your opportunity

The mission of Corporate Risk Management is to provide an integrated risk management strategy that supports the delivery of predictable financial and operational performance and produces successful client and shareholder outcomes. Corporate Risk Management serves as Schwab's second line of defense by providing independent assessments of the firm's risk, using models, controls, and systems to measure financial, operational, compliance, and legal risks to Schwab's business, employees, and customers.

In this role, your primary responsibility on the Margin Risk & Data Solutions team will be to lead data science projects focused on Schwab's margin and trading data. You will evaluate client and market data to detect risk patterns using modeling and analysis techniques, then convert that knowledge into functional models that help dictate and challenge how that risk is managed. From there, you will be responsible for model documentation, development evidence, and performance monitoring for our production models. Successful candidates will also have strong experience analyzing, manipulating, and visualizing large datasets. This is an Individual Contributor role.

This position is posted in Omaha, Chicago, Austin, Southlake, and Westlake. Qualified internal Schwab employees outside of these markets are encouraged to apply. Location flexibility for strong internal candidates may be considered based on business needs, seat availability, and organizational alignment. We encourage employees not to self-select out of consideration based solely on location.

What you have

• Bachelor's or Master's degree in Computer Science, Mathematics, Engineering, Data Science, Finance or related field.
• 5+ years of experience in model development, preferably in financial services.
• 5+ years of experience with SQL, data manipulation, and data visualization.
• Strong Python skills; experience with data analysis and manipulation frameworks (Pandas, NumPy, PySpark, etc).
• Strong fundamentals in option models and retail derivatives trading. Experience with option and equity trading models and brokerage margin policies, particularly Black-Scholes, binomial option models, value-at-risk techniques, futures SPAN margin, Monte Carlo methods, and regression.
• Ability to manage multiple deliverables and drive process improvements.
• Excellent communication skills and documentation abilities.

• Design, improve, and deploy equity option and exposure models for financial risk analytics, focusing on margin and trading data.

• Lead the management and maintenance of retail trading data sets.
• Collaborate with internal developers and architects to connect models with core banking platforms and workflows.
• Document model development, deployment processes, and integration steps for internal and external review.
• Analyze large datasets, identify risk patterns, and translate insights into actionable models.
• Present technical approaches and results to management, auditors, and business partners.
• Contribute to an Agile team, iterating quickly and delivering impactful solutions.

• Knowledge of brokerage business processes and regulatory requirements.
• Exposure to other cloud platforms (AWS, Azure, GCP) and hybrid cloud architectures.
• Experience with automation and DevOps platforms.
• Experience with C# or Java service-based architectures.
• Experience with data science and implementing machine learning

• 401(k) with company match and Employee stock purchase plan
• Paid time for vacation, volunteering, and 28-day sabbatical after every 5 years of service for eligible positions
• Paid parental leave and family building benefits
• Tuition reimbursement
• Health, dental, and vision insurance