Senior 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 Sr. Manager (AI & Data Science) you will be part of the Product Modeling team within the Treasury & Capital Markets department.  The Product Modeling team is responsible for developing data-driven analytics, forecasting, pricing and segmentation models, and generating forecasts on a recurring cadence across deposits, margins and other on-/off-balance sheet products.  

This role is a research role that is primarily focused on developing new models and enhancing existing models in that space. This position therefore requires a proven track record of publishing quantitative research (e.g. dissertation, peer-reviewed academic papers).

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;
• Be well-versed in different statistic modeling techniques as well as machine learning models, and in building fundamental quantitative models from the ground up – enabling you to condense your observations into robust models; 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:

What you have

Required skills

• Ph.D. with a strong quantitative component (Applied Math, Statistics, Engineering, Economics, Physics, Financial Engineering).
• Proven track record of publishing quantitative research.
• At least 5 years of work experience, preferably in a large financial company, as a quantitative modeler working with large and complex datasets, with consideration given for post-graduate research.
• Ability to develop new models, improve efficacy of existing models and rigorously test them.
• Solid understanding of statistical and quantitative modeling concepts and practical experience using statistical techniques to extract insights from large datasets.
• Solid theoretical understanding and hands-on experience in developing, deploying and maintaining supervised machine learning models.
• Strong programming skills in a modern programming language (e.g., Python, C++) and familiarity with object-oriented coding principle.
• 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.
• Strong work ethic, high self-motivation, proactive approach, attention to detail and ability to deliver under tight deadlines.
• Excellent communication skills, both verbally 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.
• Prior experience in developing unsupervised machine learning models.
• Prior experience in developing deep learning models.

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.
• 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.
• Familiarity with a modern software change management process.
• Apply ML models to granular and rich datasets to improve forecasts and identify customer segments.
• Leverages 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.