Data Science Intern, BS - Summer 2027

Marvell Technology•Boise, ID

12d•$29 - $57

About The Position

Marvell’s Compute and Custom Solutions (CCS) organization is seeking a Data Science Intern to join 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. It partners with business and technical teams to analyze data, develop predictive capabilities, and address real-world business challenges. The intern will contribute to a data science project that transforms historical business data into predictive insights. The work includes preparing and analyzing datasets, developing and evaluating machine learning models, and communicating methods, results, and recommendations to the team and business stakeholders.

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 libraries such as 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.

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 modern and classical techniques such as 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 data quality issues, inconsistencies, and missing values.
Analyze historical datasets to identify trends, relationships, outliers, and features that may contribute to predictive performance.
Develop and train machine learning models using Python and established data science libraries to predict business outcomes from historical data.
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.