Applied Machine Learning Scientist Intern - PhD

Marvell Technology•Santa Clara, CA

About The Position

Marvell IT - Data Analytics Team is at the forefront of driving Marvell’s Data Intelligence, Strategy, and Architecture initiatives. As a high-visibility team, we deliver critical data insights and analytics solutions to the operations and leadership group that empower data-driven decision-making at the highest levels. Our team is responsible for executing global, high-impact projects that add significant value across the organization. By leveraging cutting-edge data analytics technologies and methodologies, we ensure that Marvell remains a leader in harnessing data for business advantage. We are seeking a motivated and enthusiastic Data Science Intern to join our data and analytics team. This internship is an excellent opportunity for individuals looking to apply their academic knowledge of data science in a real-world setting. The intern will work closely with our experienced data scientists, engineers, and business stakeholders to develop data-driven insights and build predictive models that address business challenges.

Requirements

Pursuing degree in Computer Science, Data Science, or related field
Proficiency in Python for data analysis, including libraries like pandas, NumPy, scikit-learn, and matplotlib etc.
Knowledge of SQL for querying databases.
Understanding of basic statistical concepts and machine learning algorithms (e.g., regression, classification, clustering).
Familiarity with data visualization tools such as Power BI or Python-based visualization libraries (e.g., seaborn, Plotly).
Proficiency in data preprocessing, data transformation, and exploratory data analysis (EDA) to extract valuable insights from raw datasets.

Nice To Haves

Familiarity with Generative AI and Large Language Models (LLMs) is a plus.

Responsibilities

Collaborate with the data science team to gather, clean, and preprocess data from various sources.
Perform exploratory data analysis (EDA) to identify patterns, trends, and relationships within large datasets.
Assist in building and optimizing machine learning models to solve business problems.
Develop data visualizations, dashboards, and reports using tools like Power BI or Python libraries.
Present findings and recommendations to stakeholders in a clear and concise manner.
Work with large-scale data sets using SQL, Python for data manipulation and analysis.
Support the development and deployment of machine learning models in production.
Support data integration and data engineering activities, including ETL processes, within Snowflake or Databricks environments.