Director, Quantitative Software Engineer – Research Lead

Charles Schwab

San Francisco (CA)

On-site

USD 180,000 - 260,000

Full time

4 days ago

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Benefits offered by this job

Bonus eligibility

401(k) with company match

Sabbatical after 5 years

Parental leave and family benefits

Tuition reimbursement

Health, dental, and vision insurance

Job summary

Charles Schwab in San Francisco seeks a Director, Quantitative Software Engineer - Research Lead to partner with quantitative researchers, investment professionals, and technology teams to accelerate research, model development, and product delivery. You will design scalable research platforms and data pipelines, transforming investment concepts into production-ready solutions.

The ideal candidate has 8+ years of software engineering experience, a strong background in Python or similar

Qualifications

Bachelor's degree in Computer Science, Engineering, Mathematics, Quantitative Finance, or related technical field or equivalent practical experience.
8+ years of software engineering experience developing quantitative applications, research platforms, or data-intensive solutions using Python and/or similar languages (R, Julia, MATLAB).
Experience partnering directly with quantitative researchers, portfolio managers, or investment professionals.
Experience designing, building, and supporting scalable data pipelines and analytical platforms.
Knowledge of quantitative research workflows (data acquisition, model development, backtesting, validation, deployment).
Experience designing data models, data integration solutions, and data storage architectures.
Experience developing data visualization and analytical tools supporting research and decision-making.
Experience implementing data governance, lineage tracking, monitoring, and data quality controls.
Ability to balance research flexibility with reliability, reproducibility, security, and supportability; strong communication skills.

Responsibilities

Partner with researchers and investment professionals to accelerate research, model development, and product delivery.
Design scalable data platforms and reusable research capabilities for production deployment.
Lead complex technical initiatives and drive collaboration across teams.
Ensure data governance, quality controls, and reliable, reproducible research workflows.