Data Science Intern, BS - Summer 2027

Marvell Technology•Boise, ID

10d

About The Position

Marvell’s Compute and Custom Solutions (CCS) organization is hiring a Data Science Intern for its Business Automation team. The team applies data science, AI, and intelligent automation to improve business processes, increase efficiency, and support data-driven decision-making across CCS. The intern will contribute to a project that transforms historical business data into predictive insights and will work with business and technical teams on real-world business challenges.

Requirements

Currently pursuing a bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field, with an expected graduation date between Fall 2027 and Summer 2028.
Proficiency in Python for data processing, analysis, and machine learning, including experience with pandas, NumPy, and scikit-learn.
Strong foundation in statistics, exploratory data analysis, feature engineering, and machine learning fundamentals.
Experience working with structured datasets and using SQL to query, manipulate, and analyze data.
Ability to analyze technical results and clearly communicate findings to technical and non-technical audiences.
Applicants must be eligible to access export-controlled information under applicable law; some applicants may be subject to an export license review process.

Nice To Haves

Experience developing and evaluating predictive models using regression, classification, decision trees, random forests, or gradient boosting.
Experience developing and evaluating predictive models using LLM inference, embedding models, SVM, or XGBoost.
Familiarity with Git or similar source-control and collaborative development tools.
Prior academic, research, or project experience using real-world datasets to develop machine learning or predictive analytics solutions.

Responsibilities

Clean and prepare historical business data for analysis and machine learning, identifying and resolving quality issues, inconsistencies, and missing values.
Analyze historical datasets to identify trends, relationships, outliers, and features relevant to predictive performance.
Develop and train machine learning models in Python using established data science libraries to predict business outcomes.
Evaluate and compare modeling approaches using appropriate statistical and machine learning techniques to assess accuracy and reliability.
Document and present methodology, model performance, findings, and recommendations to the Business Automation team and business stakeholders.