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Internship Machine Learning Finance Jobs in Santa Rosa, CA

... machine learning, and AI-driven decision-making. * Curriculum Development: Design and refine AI ... Familiarity with AI applications in industries such as finance, healthcare, marketing, and ...

Square makes commerce and financial services accessible to sellers. Cash App is the easy way to ... You will work side by side with engineering, machine learning, data, design, and operations to turn ...

New

Head of AI & Data

Bodega Bay, CA · On-site

$135K - $163K/yr

Strong background in machine learning, data engineering, or applied AI * Experience working with ... Experience in fintech, financial data, or regulated environments * Exposure to scalable cloud data ...

Strong background in machine learning, data engineering, or applied AI * Experience working with ... Experience in fintech, financial data, or regulated environments * Exposure to scalable cloud data ...

Financial & Operational Optimization • Identify opportunities for cost savings, vendor ... talent, machine learning algorithms, and artificial intelligence to provide customized talent ...

... internship beyond the harvest season. Weekly team lunches will be provided as well as many ... Ability to safely operate various machines * Able to stand for long periods of time Company ...

Technical Program Manager

Bodega Bay, CA · On-site

$153K - $198K/yr

... Finance teams. Ensure clear ownership, accountability, and handoffs across teams. Coordinate ... talent, machine learning algorithms, and artificial intelligence to provide customized talent ...

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Internship Machine Learning Finance information

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How much do internship machine learning finance jobs pay per hour?

As of Jun 7, 2026, the average hourly pay for internship machine learning finance in Santa Rosa, CA is $21.71, according to ZipRecruiter salary data. Most workers in this role earn between $18.65 and $24.42 per hour, depending on experience, location, and employer.

What are Internship Machine Learning Finance positions?

Internship Machine Learning Finance positions are temporary roles where students or recent graduates work with financial organizations to apply machine learning techniques to solve finance-related problems. Interns may analyze large datasets, build predictive models, automate trading strategies, or detect fraud using machine learning algorithms. These internships provide hands-on experience in both finance and artificial intelligence, helping interns develop technical and industry-specific skills. They often require a background in programming, statistics, and a basic understanding of financial concepts.

What types of projects do interns typically work on in a Machine Learning Finance internship?

As a Machine Learning Finance intern, you can expect to work on a variety of projects that blend quantitative analysis with practical financial applications. Common responsibilities include developing predictive models for stock prices or credit risk, analyzing large financial datasets, and building tools to automate trading strategies or detect fraud. Interns often collaborate closely with data scientists, software engineers, and finance professionals, gaining exposure to both technical and business aspects of the field. This hands-on experience is invaluable for building real-world skills and understanding the fast-paced finance environment.

What are the key skills and qualifications needed to thrive as an Intern in Machine Learning Finance, and why are they important?

To thrive as an Intern in Machine Learning Finance, you need a foundational understanding of statistics, programming (especially Python or R), and financial concepts, often supported by progress toward a quantitative degree. Familiarity with machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), data analysis tools, and version control systems like Git is typically expected. Strong analytical thinking, problem-solving abilities, and effective communication help you translate technical results into actionable financial insights. These skills are critical for developing robust models, supporting data-driven decision-making, and contributing meaningfully within interdisciplinary finance teams.

What is the difference between Internship Machine Learning Finance vs Data Analyst Intern?

AspectInternship Machine Learning FinanceData Analyst Intern
Required SkillsProgramming (Python, R), Machine Learning, Finance knowledgeData analysis, SQL, Excel, basic statistics
Work EnvironmentFinance firms, tech-driven finance teamsFinancial institutions, consulting firms, tech companies
Industry UsageFinance, Fintech, Quantitative researchFinance, marketing, consulting

Internship Machine Learning Finance focuses on applying machine learning techniques to financial data, requiring programming and finance knowledge. Data Analyst Internships involve analyzing data sets, creating reports, and using statistical tools. Both roles are common in finance-related industries but differ in technical focus and skill requirements.

What are popular job titles related to Internship Machine Learning Finance jobs in Santa Rosa, CA? For Internship Machine Learning Finance jobs in Santa Rosa, CA, the most frequently searched job titles are:
What job categories do people searching Internship Machine Learning Finance jobs in Santa Rosa, CA look for? The top searched job categories for Internship Machine Learning Finance jobs in Santa Rosa, CA are:
What cities near Santa Rosa, CA are hiring for Internship Machine Learning Finance jobs? Cities near Santa Rosa, CA with the most Internship Machine Learning Finance job openings:
Staff Machine Learning Engineer, Credit Products (Square Financial Services)

Staff Machine Learning Engineer, Credit Products (Square Financial Services)

Block

Bodega Bay, CA • On-site

Other

Posted 22 days ago


Block rating

7.9

Company rating: 7.9 out of 10

Based on 16 frontline employees who took The Breakroom Quiz

9th of 17 rated payment service providers


Job description

Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams - People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more - provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block.

The Role

The Credit and Lending team is responsible for the predictive intelligence that underpins Block's primary capital-intensive products. These products unlock unique access to credit for our customers, many of whom are otherwise underbanked and underserved by the traditional financial system. As a Machine Learning Engineer within Square Financial Services (SFS), you will occupy a high-leverage role at the intersection of regulated banking and advanced autonomous systems. This position requires full-stack ownership of the credit engine, from the curation of novel data signals to the implementation of the decisioning logic that drives Block's top-line growth.
Our credit products are material drivers of the company's profitability and are frequently highlighted in executive reviews and quarterly earnings reports. We are seeking a scientifically-minded contributor capable of delivering extraordinary individual leverage to expand our underwriting capabilities into previously untapped segments through pragmatic policy evolution and advanced modeling techniques.

You Will

  • Apply a rigorous scientific mindset to the challenge of underwriting new customer segments, involving the evaluation of alternative external data sources and the deployment of advanced architectures to enhance predictive accuracy.
  • Lead complex ML Operations and Infrastructure initiatives that advance our modeling capabilities, such as scaling data ingestion or enabling the use of more complex neural networks.
  • Design and implement the full credit modeling stack, taking responsibility for the entire lifecycle of credit decisioning and ensuring models are robustly integrated into production environments.
  • Use data science techniques to leverage new data sources for modeling, making sense of messy datasets and bringing clarity to business decisions.
  • Identify and execute material improvements to credit policy, applying an analytical lens to determine where technical or logic shifts can yield significant positive outcomes for the customer and the bank's portfolio.
  • Support team members in ad-hoc and scheduled updates to existing models, and help troubleshoot issues in a real-time production environment.
  • Operate effectively within the framework of a regulated bank (SFS), balancing rapid innovation with the requirements of safety, soundness, and compliance.

You Have

  • Minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years experience, with a focus on developing and deploying machine learning and statistical models in production environments.
  • A degree in a technical field (e.g., Computer Science, Mathematics, Statistics, Physics, or Engineering). We have a strong preference for candidates with a demonstrated track record of scientific research or an advanced degree.
  • Strong quantitative intuition and data visualization skills, with a proven ability to conduct sophisticated ad-hoc and exploratory analysis.
  • Full-stack proficiency preferred, including the ability to contribute across the entire technical stack-from data pipelines to production-grade software architecture.
  • The versatility to communicate clearly with both technical and non-technical audiences, particularly in the context of high-visibility projects and executive stakeholders.
  • A pragmatic approach to problem-solving, with a willingness to utilize whichever tool is most appropriate for the situation while balancing complex business, technical, and regulatory constraints.
  • Experience with tree-based models and gradient boosting is helpful but not required; we value the ability to adapt and learn new methodologies as the credit landscape evolves.

We're working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances.

We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page.


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