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.
Schwab’s Wealth and Asset Management (WAM) Engineering organization is a part of Schwab Technology Services that is responsible for Schwab's wealth, advice, investment management, and research platforms. Everything that WAM Engineering builds serves Schwab’s mission of helping clients reach their financial goals.
As a Manager, Quantitative Software Engineer for WAM Engineering Research Technology, you will work as a hands-on technologist focused on our quantitative research initiatives. We currently support a variety of existing on-premise and cloud solutions, and we are actively expanding the capabilities of these platforms to drive cutting-edge research and product development activities.
The role requires hands-on development in a client-driven technology organization with a focus on regulatory, tactical, and strategic business initiatives. The candidate will be responsible for evolving our key data platforms and delivering models, analytics, and reporting projects. The ideal candidate is expected to be a self-starter who can take responsibility for building significant capabilities within the broader research platform. This role works within a mature Agile and DevOps model in partnership with our business stakeholders, requiring active engagement with Product Owners, Researchers, Architects, and other Partners — in managing requirements, design, coding, testing (unit and functional), deployment, and post-release support.
What you will do:
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 testsApply 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 partnersInfluence and implement improvements and efficiencies in both the technical and non-technical aspects of the development processWhat you have
To ensure that we have fulfilled our promise of "challenging the status quo," this role has specific qualifications that successful candidates should have.
Required Qualifications
Master’s degree in computer science, information systems, math, engineering, or other technical field, or equivalent experience.Seven or more years of experience with Python or Java.Proficiency in one or more programming languages used for model development and analysis (e.g Python or R).Experience with analytics dashboard visualization apps using Python, e.g. Plot.ly Dash.Experience implementing multi-factor equity models for investment decision-making.Three or more years of experience in building data lake solutions working with large financial research timeseries datasets. Proficiency in designing quant-research optimized data models that support efficient data retrieval and aggregation of financial datasets, including complex hierarchical structures.Working experience with Schwab procedures and processesFamiliarity in developing distributed data processing and streaming frameworks and architectures. Familiarity with NoSQL database technologies (e.g. MongoDB, BigTable, DynamoDB).Experience leveraging continuous integration/development tools (e.g. Jenkins, Docker, Containers, OpenShift, Kubernetes, and container automation) in a Ci/CD pipeline.Preferred Qualifications
Experience delivering data platform modernization efforts in a investment management or other financial services organization.Attention to detail and results oriented, with a strong customer focus.Ability to prioritize workload to meet tight deadlines.Ability to conceptualize the best tactical approach for a team to deliver a project while defining the target state evolution path.Strong communication skills and capability to interface with senior business stakeholders.Self-motivated as well as creative and efficient in proposing solutions to complex, time-critical problems.
In addition to the salary range, this role is also eligible for bonus or incentive opportunities
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