PhD Machine Learning Research Intern

Job Description

PhD Machine Learning Research Intern | Experian

The Tone:
This is an internship at Experian, a global data and technology company. Experian builds solutions that empower opportunities for people and businesses, redefining lending, preventing fraud, simplifying healthcare, creating digital marketing solutions, and gaining insights into various markets. The role is a fully remote position within the North America R&D Data Lab, where you will contribute to research and development of novel analytical solutions and new product prototyping, playing a key role in advancing the company’s data and analytics capabilities.

The TL;DR
• Role: Internship
• Location: Remote

• Team: Experian North America R&D Data Lab
• Mission: Research and develop novel analytical solutions, prototype new products, and evaluate/acquire new data assets using advanced machine learning and data mining techniques.
• Tech Stack: pytorch, Keras, tensorflow, scikit-learn, pandas, Spark, pySpark, Python, R, Java, C++, C

What You’ll Actually Do
• Develop: Create advanced machine learning analytical solutions to extract insights from diverse structured and unstructured data sources.
• Apply: Select and apply the right machine learning, deep learning, and processing techniques to unearth data value.
• Design: Refine data manipulation and retrieval through the design of efficient data structures and storage solutions.
• Analyze: Dissect and document vast datasets, processing them to highlight patterns and insights, and solve complex challenges by developing impactful algorithms.
• Validate: Ensure model excellence by validating performance scores, analyzing ROI, and articulating processes and outcomes through documentation and presentations.

The Must-Haves
• Background: Doctorate Degree. Currently enrolled in a PhD degree program in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Math, or other quantitative fields, with a plan to return to school in Fall 2026 to complete the program.
• Experience: Background in analytics, data mining, and/or predictive modeling. Experience modifying and applying advanced algorithms to address practical problems. Experience with deep learning (CNN, RNN, LSTM, attention models, etc.), machine learning methodologies (SVM, GLM, boosting, random forest, etc.), graph models, and/or reinforcement learning. Experience with large data analysis using Spark.
• Skills: Proficient with open-source tools for deep learning and machine learning technology such as pytorch, Keras, tensorflow, scikit-learn, and pandas. Proficient in more than one of Python, R, Java, C++, or C.
• Bonus: Experience with pySpark is preferred for large data analysis.