Internship Program, Business Intelligence at Red Bull

Santa Monica, California, United States -

Full Time

Start Date

Immediate

Expiry Date

14 Jun, 26

Salary

33.0

Posted On

16 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Python, Analytics, LLM, Sentiment Analysis, Text Parsing, Data Infrastructure, Snowflake, Data Validation, Stakeholder Collaboration

Industry

Food and Beverage Services

Description

Company Description Candidates are encouraged to apply to 2 or fewer internship roles during a given internship recruiting cycle. Are you excited by the idea of turning complex data into clear, actionable insights that improve how a business operates in the real world? As an intern on the Distribution Services Business Intelligence team, you will support impactful projects that deepen our understanding of sales feedback and data infrastructure. You’ll explore text-based feedback using Large Language Models (LLM), connect key sales metrics, and help optimize our Snowflake data environment. In doing so, you’ll build core skills in SQL, Python, and analytics while collaborating with stakeholders across Sales, Operations, & Talent. Job Description SENTIMENT ANALYSIS & TEXT PARSING Examine managers’ free-text in-store assessments from around the country using Python & LLM packages. Determine a sentiment analysis of manager responses. Evaluate response quality by assessing the impact of length, keywords, and grouping similar assessments using different synonyms. Determine the impact of the highest-quality responses in their respective markets. COMPARATIVE ANALYSIS Inspect historical sequential datasets and help find the tangible numeric impact between each dataset on a granular store level. Connect Offtake, Depletions, & Backstock to build a full picture of the sales path of a can of Red Bull. Uncover potential causal factors between datasets, measuring the relationship & impact each individual metric has. DATA TABLE VALIDATION (SNOWFLAKE) Support data tables review, ensuring that large-scale tables from related / different sources match each other; Data table sizes ranging from 10k – 300M rows. Learn to establish automated system monitors, warning of data issues as soon as they occur. Apply theoretical data structures to practical issues, synchronizing large datasets used for everyday business. Qualifications Actively enrolled in college/university as an undergraduate or graduate student Available to work full-time (40 hours per week) for the duration of the internship Demonstrated initiative through academics, work experience, or extracurricular involvement Comfortable writing python code, experience in LLM is a plus Familiarity with SQL A growth mindset, with the ability to learn quickly, adapt to new environments, and incorporate feedback Additional Information The compensation for this position is $33 per hour. Our current Benefits include: Comprehensive Medical, Dental and Vision Plans, 401k Match, Family Leave, PTO & Paid Holiday Schedule, Pet, Legal, and Life Insurance, Tuition Reimbursement (Benefits listed may vary depending on the nature of your employment and/or work location) Red Bull North America, Inc. is an Equal Opportunity Employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status, age, or any other classification protected by Federal, state, or local law. We will consider for employment all qualified Applicants, including those with Criminal Histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance. Travel: 0 Benefits Eligible: No Organization: Red Bull Distribution Company Job type: Temporary

Responsibilities

Interns will support impactful projects involving sales feedback and data infrastructure, including exploring text-based feedback using Large Language Models and connecting key sales metrics. Responsibilities also cover examining manager assessments via sentiment analysis and evaluating response quality, alongside inspecting historical datasets to find numeric impacts between metrics like Offtake, Depletions, and Backstock.