Manager, Treasury Modeling Researcher

Your Opportunity

At Schwab, you're empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us "challenge the status quo" and transform the finance industry together.

As a Manager (Treasury Forecasting Architecture) you will be part of the Product Modeling team within the Treasury Modeling department. The Product Modeling team is responsible for developing forecasting, pricing, and segmentation models across deposits, margins, and other on-/off-balance sheet products, combining structural modeling, quantitative research, and data-driven analytics to support recurring business forecasts.

This role is a Research/Modeling role primarily centered on developing new models and enhancing existing models in that space. The position will require competent coding of experience in model development cycle and deployment.

To succeed in this role, you should:

• Possess solid programming and data manipulation skills, allowing you to extract valuable insights from large datasets and to write production-level code under a rigorous change management process;
• Have deep expertise in structural and mechanistic modeling, together with strong statistical modeling skills, and the ability to build quantitative models from the ground up; machine learning experience is helpful but not the primary focus of the role; and
• Be an excellent team player and a trusted advisor collaborating with other team members as well as be ready to present your results and observations to a wide group of stakeholders.

If you are looking for a role where you can leverage all your technical skills while having a direct business impact and want to be part of a very impactful and driven team within the Treasury organization, we encourage you to apply.

What you have

Required skills

• Bachelor Degree in Economics (focus on econometrics), Statistics, Mathematics or other related scientific fields.
• Understanding of basic model architecture and model deployment through the life cycle of production ready code.
• At least 2 years of work experience, preferably in a large financial company, as a developer or quantitative modeler working with large and complex datasets.
• Ability to develop new models, improve efficacy of existing models and rigorously test them.
• Deep understanding of structural, statistical, and quantitative modeling concepts, with practical experience developing models grounded in underlying system dynamics, economic behavior, or other interpretable drivers.
• Proven track record in developing, deploying and maintaining models.
• Strong programming skills in a modern programming language (e.g., Python, C++) and familiarity with object-oriented coding principles.
• Experience with a modern software change management process.
• Advanced experience in extracting data from relational databases (e.g., via SQL).
• Experience in visualizing data analytics and model results.
• Excellent communication skills, both verbal and written.
• Strong inter-personal skills and a collaborative team player.

Preferred skills

• Prior work experience in a Treasury department of a large financial firm.
• Prior experience modeling deposits, margin, and other balance sheet products.
• Experience applying structural or mechanistic models in a production environment.
• Experience with time-series, dynamic systems, or behavioral modeling in applied settings.

What you''ll do:

• Gain a deep understanding of the various forecasting models which measure the market risk in financial projections associated with a diverse set of products across the balance sheet.
• Architect models into Forecast Stack for various forecast assumptions and model-as-a-service api use cases.
• Analyze large datasets to extract insights into Schwab's balance sheet and clients, and to provide analytical support for strategic decisions.
• Develop new models for margin and deposit products. This includes research, writing of model white papers and technical papers and model maintenance.
• Develop and refine structural and quantitative models using granular datasets to improve forecasts and understand customer and product behavior; apply machine learning techniques selectively where they provide incremental value.
• Leverage industry opportunities to expand the organization's capabilities in data science, engineering, and modeling techniques.

In addition to the salary range, this role is also eligible for bonus or incentive opportunities.