Okay, I've searched for full-time Data Scientist roles focused on platform automation and analytics in Lone Tree, CO, within the financial services industry, requiring a Bachelor's degree in Computer Science and 3+ years experience in Python, SQL, and ETL development. I have found 27 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Scientist role within Schwab’s Treasury Finance team is fundamentally about leveraging advanced data engineering and analytic solutions to support critical financial processes such as net interest revenue forecasting and market risk management for a colossal $500 billion balance sheet portfolio. The core purpose is to architect, develop, and maintain scalable, automated data pipelines and BI tools that empower accurate, timely, and actionable financial insights. The candidate will need to blend technical prowess in ETL, Python, SQL, and DevOps with strong problem-solving skills to build robust, modular frameworks for data quality control and automation. Because the role sits at the intersection of finance and technology, familiarity with cloud platforms, Agile methodologies, and financial services best practices is essential to navigate complexity and regulatory influences. The candidate will encounter challenges around data integrity, cross-team coordination, and ensuring system reliability within a high-stakes, fast-evolving financial environment. Success looks like delivering efficient, automated platforms that reduce manual effort, improve forecasting accuracy, and support strategic treasury decision-making, while mentoring peers and collaborating fluidly across technical and business domains.

Company Analysis:

Charles Schwab stands as an innovative disruptor in the investment services industry, priding itself on customer-centric, technology-driven financial solutions. Its culture emphasizes empowerment, challenging industry norms, and continuous improvement, which dovetails well with a data scientist’s mandate to innovate and optimize complex processes. Schwab’s hybrid work model and strong benefits reflect a commitment to employee well-being and development, fostering both autonomy and collaboration. Being part of a Treasury team managing huge balance sheet assets means this role provides substantial visibility and strategic importance, bridging finance and tech to influence key metrics like risk and revenue. The company’s focus on mission-driven impact and a supportive culture suggests that candidates who thrive will be those who embrace learning agility, take initiative, and value cross-functional teamwork. This role is a strategic hire aimed at scaling automation and analytic capabilities to keep pace with market dynamics and regulatory requirements, positioning the candidate as a technical leader contributing directly to Schwab’s differentiated client experience and financial stewardship.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to design and implement an automated data pipeline. What challenges did you face and how did you overcome them?
• Behavioral: Describe a situation where you identified a critical data quality issue. How did you detect it and what steps did you take to resolve it?
• Behavioral: Can you share an experience working in a cross-functional team with business and technology partners? How did you ensure alignment and successful delivery?
• Behavioral: Give an example of when you had to mentor or support junior team members on technical tasks. How did you balance guidance with hands-on involvement?
• Technical: How would you approach building a modular QA/QC framework for automated anomaly detection in ETL pipelines?
• Technical: Walk me through your experience with Python and SQL in developing data engineering applications. How do you ensure your code is maintainable and scalable?
• Technical: What strategies do you use to optimize data model performance, particularly in large-scale financial datasets?
• Technical: Can you explain how you have implemented DevOps best practices such as CI/CD in your previous data engineering projects?
• Situational: Imagine you discover inconsistent data outputs from an ETL pipeline critical to net interest revenue forecasting shortly before a key deadline. What steps do you take?
• Situational: If tasked with automating manual financial reporting processes, how would you collaborate with stakeholders to gather requirements and ensure successful adoption?
• Situational: You identify a new technology or tool not currently used by your team that could significantly improve automation. How would you propose and lead its adoption?
• Company Fit: Why are you interested in working for Charles Schwab, particularly within the Treasury’s data and analytics team?
• Company Fit: Schwab emphasizes a culture of challenging the status quo and innovation. How does your approach to problem-solving align with these values?
• Company Fit: How do you see your technical skills contributing to Schwab’s mission of enhancing the investing experience for clients through technology?
• Questions to Ask Interviewer: What are the biggest challenges currently facing the Treasury team in terms of data infrastructure and automation?
• Questions to Ask Interviewer: Can you describe how collaboration typically works between data scientists, finance professionals, and technology teams here at Schwab?
• Questions to Ask Interviewer: How does Schwab support the ongoing professional development and upskilling of technical roles like this one?