At Charles Schwab, our purpose is simple: we champion client’s goals with passion and integrity. Guided by honesty, mutual respect and a commitment to doing what’s right, we bring innovation, education, and service together to help shape financial futures. Our people are the foundation of our success – they approach their work with curiosity and collaboration, coming together to create solutions that make a meaningful impact for clients and communities. As we expand into India, we are bringing this same culture of inclusion, learning, and opportunity to new talent. Joining us means becoming part of a global team where your work matters and your future can take shape.
Our Hyderabad location is central to Schwab’s growth, bringing together talented people and technology to drive innovation, scale and efficiency. Here, you will work alongside teams who create solutions that support millions of clients every day. The work you do is more than daily operations – it’s a chance to experiment, learn, and build within a values-driven, supportive environment. This is a unique opportunity to be part of our early growth phase and shape something new, backed by the stability and strength of a Fortune 500 company. Your impact begins on day one, and your contributions will help define our future in the region.
Key Responsibilities:
Design, implement and maintain software solutions that enable quantitative research
Apply knowledge and take a lead role in implementing quantitative investment research products within the designated business area to support organizational goals and objectives, including gathering and collecting time series financial data, and developing quantitative models, analytics, and tests
Apply knowledge and take a lead role in implementing quantitative systems architecture within the designated business area to support organizational goals and objectives, including designing and maintaining financial research data systems that leverage distributed/cloud computing, data analytics, and machine learning.
Develop control frameworks required to deploy and manage quant models in production environments.
Lead data engineering implementation efforts for data processing pipelines that curate financial datasets into representations fit for model research and production applications.
Co-develop production code in conjunction with quant researchers to deliver performant software (supported by comprehensive test coverage) that meet investment objectives while minimizing the risk of model errors; manage SDLC processes to enable CI/CD.
Validate model outputs and defines controls; document model hypotheses including goals, back-testing, and stress-testing strategies; support model optimization analysis.
Design, develop, test, and deploy systematic analytics pipelines using machine learning / statistical analysis frameworks.
Build data visualizations and other tooling to enable model researchers to efficiently monitor model performance and stability, as well as to interpret model outputs.
Assist in selection and integration of data related tools, frameworks, and applications required to expand platform capabilities.
Lead or participate in developing proof-of-concepts to understand the appropriateness of new methodologies and/or technologies for our platforms.
Analyze business problems and processes; help refine requirements and design solutions for them. Be a champion of new ways of collaborating with technology and business partners
Influence and implement improvements and efficiencies in both the technical and non-technical aspects of the development process.
Required Qualifications:
Seven plus years of experience with Python or Java in complex operating environments
Proficiency in one or more programming languages used for quantitative investment model development and analysis (e.g Python or R)
Experience designing, building, and supporting data pipelines and related processes with large financial research timeseries datasets, including implementing robust exception-handling, observability, and quality control capabilities
Proficiency in designing quant-research optimized data models that support efficient data retrieval and aggregation of financial datasets, including complex hierarchical structures
Familiarity in developing distributed data processing and streaming frameworks and architectures
Experience leveraging continuous integration/development tools (e.g. Jenkins, Docker, Containers, OpenShift, Kubernetes, and container automation) in a CI/CD pipeline
Experience operating in a regulated environment with a focus on delivering systems that are compliant with legal and ethical data standards
Familiarity with incorporating statistical techniques and hypothesis testing into the analysis of large datasets to support decision-making
Expertise in quantitative systems architecture to enhance the business impact of software solutions
Experience implementing software development testing and verification capabilities to enhance component reliability and address operational risks
Understanding of investment management principles and concepts; strong interest in becoming a domain expert
Practical experience in leveraging Generative AI capabilities to enhance productivity and efficiency, with sufficient experience to understand the pitfalls of blindly relying on such technologies
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Charles Schwab Corporation is a financial services company that provides banking, securities, and investment advisory services.