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AI & Data Science Intern

Gardner Capital, Inc.
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AI & Data Science Intern

 

Gardner Capital is seeking a well-qualified AI & Data Science Intern to support our operations. Gardner Capital is an Affordable Housing and Solar developer, investor and tax credit syndicator with a nationwide presence. The Intern will spearhead AI research, solutions and applications, as well as identify, develop and implement predictive modeling solutions for Gardner Capital. 

The position is a part-time, paid internship requiring on average 16–24 hours per week. This is an in-person role based in our St. Louis, MO office.

 

Job Duties

• Candidates will spearhead AI research projects, exploring new applications to improve operational efficiencies. 

• Assist Gardner Capital in identifying opportunities to build an internal data science function
• Use programming skills to explore, analyze and interpret large volumes of structured and unstructured data
• Validate and clean data from multiple sources. Ensure data quality and integrity for effective modeling
• Acquires data from multiple data sources to perform analysis

• Develop and maintain small tools or scripts (e.g., in Python, VBA, or similar) to automate data extraction from PDFs and other reports into Excel models and internal databases.
• Lead forward-looking/predictive analytics
• Help with bringing analytics expertise in-house

 

Preferred Knowledge, Skills and Abilities

• Strong knowledge of AI analytical skills
• Excellent oral and written communication skills
• Self-starter who is comfortable with both taking initiative and working in collaboration
• Experience in solving analytical problems using quantitative approaches
• Knowledge and experience with data technologies and programming languages (i.e. Macros, VBA, Python, SQL).

• Experience packaging analytics or data workflows into simple tools (such as scripts, APIs, or lightweight web apps) so non-technical users can easily run them.
• Detail oriented

 

Preferred Education and Experience

• Currently enrolled and pursuing a degree in math, statistics, engineering, computer science or related fields. Prefer junior, senior or graduate student class level.

• GPA of 3.0 or higher