Data Scientist

About The Company

Headquartered in San Diego, Mulligan Funding is a financial services company focused on providing funding solutions to businesses. The company uses data and analytical insights to support informed credit decisions and help guide its operations. As Mulligan Funding expands its data science team, it is seeking a curious and analytical Data Scientist I Management Trainee who is eager to develop practical experience working with business data.

About The Role

The Data Scientist I Management Trainee will contribute to quantitative analysis and data-driven decision-making across the organization. This is an opportunity for an early-career professional to apply SQL and Python skills to business questions, examine information relevant to credit strategy, and work with colleagues in Risk and Operations. You will help transform data into clear findings and actionable recommendations, while building an understanding of how analytical work supports funding decisions and operational processes.

In this role, you will also engage with data vendors and explore how external data sources can provide useful perspectives. Success will require sound analytical thinking, attention to detail, effective communication, and an interest in learning how data science can address practical business challenges. You will work collaboratively, ask thoughtful questions, and present findings in a way that helps colleagues understand both the results and their potential implications.

Qualifications

Candidates should hold a bachelor’s degree in Mathematics, Statistics, or a related quantitative field. Strong SQL skills are required, along with proficiency in Python and an ability to use these tools to query, prepare, and analyze data. Applicants should be comfortable approaching quantitative questions methodically, checking their work for accuracy, and explaining analytical results clearly.

The ideal candidate is curious, dependable, and motivated to grow in a data science environment. Strong problem-solving skills and careful attention to detail will help you work effectively with complex information. The role also calls for clear written and verbal communication, a collaborative approach, and the ability to connect analytical findings to business needs. An interest in credit analysis, risk management, business operations, or data-driven decision-making will be valuable.

Responsibilities

Perform SQL queries to retrieve and organize data for analysis, and use Python to support quantitative investigations. Examine datasets for patterns, trends, and relevant signals that may inform credit strategies. Translate analytical findings into practical insights and recommendations for colleagues, presenting results clearly and with appropriate context.

Collaborate with Risk and Operations teams to understand their questions, clarify analytical objectives, and deliver work that supports business decisions. Contribute to the evaluation of data sources and engage with data vendors to explore information that may strengthen analytical approaches. Maintain careful, organized work practices, validate data and results where appropriate, and communicate assumptions or limitations. Continue developing technical and business knowledge through feedback, collaboration, and hands-on analytical assignments.

Benefits

This position offers the opportunity to begin or advance a career in data science while working on real business and credit-related questions. You will gain exposure to collaboration across Risk and Operations, apply SQL and Python to practical analyses, and learn how data can inform organizational strategies. The role also provides opportunities to engage with data vendors and broaden your understanding of analytical methods and business applications. Specific compensation, benefits, and employment details can be discussed during the hiring process.

Equal Opportunity

Mulligan Funding is committed to fair and respectful consideration of applicants. Candidates should be evaluated based on their qualifications, skills, experience, and ability to contribute to the role. We welcome applicants who are interested in applying quantitative thinking and developing their capabilities in a collaborative data science environment.