Machine Learning Engineer Intern

Job Description

Machine Learning Engineer Intern | Experian

The Tone:
This is a fully remote summer internship at Experian, a global data and technology company listed on the London Stock Exchange. Experian powers opportunities by helping redefine lending, uncover and prevent fraud, simplify healthcare, create digital marketing solutions, and gain deeper insights into various markets, all through their unique combination of data, analytics, and software. This role is part of the Experian Summer Internship Program, which invests in students to apply their education to real-world challenges and meaningful, hands-on projects within the company’s R&D unit. The program is rooted in a ‘People First’ philosophy, ensuring interns experience a commitment to personal and professional development.

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

• Team: The Experian NA Innovation Lab, an R&D unit specifically formed to collaborate with Experian’s teams, enhance client relationships, and acquire strategic datasets.
• Mission: This role is part of the Experian North America R&D Data Lab, concentrating on the research and development of novel analytical solutions, new product prototyping, and the evaluation and acquisition of new data assets to help organizations manage customer relationships.
• Tech Stack: Pytorch, Keras, Tensorflow, Scikit-learn, Pandas, Spark, PySpark, Python, R, Java, C++, C.

What You’ll Actually Do
• Solution Design: Create advanced machine learning analytical solutions to extract insights from diverse structured and unstructured data sources.
• Data Application: Select and apply appropriate machine learning, deep learning, and processing techniques to uncover data value.
• Data Structure: Design efficient data structures and storage solutions to refine data manipulation and retrieval.
• Algorithm Development: Solve complex challenges by developing impactful algorithms.
• Performance Analysis: Validate model performance scores and analyze ROI and benefits to ensure model excellence.

The Must-Haves
• Background: Early Career. Currently pursuing a PhD in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Math, or a related quantitative field, with a plan to return to school in Fall 2026 to complete the degree program.
• Experience: Prior experience in analytics, data mining, and/or predictive modeling. Demonstrated experience modifying and applying advanced algorithms to address practical problems. Proven background in deep learning methodologies such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and attention models. Familiarity with machine learning techniques including Support Vector Machines (SVM), Generalized Linear Models (GLM), boosting, and random forest. Experience with graph models and/or reinforcement learning. Expertise in large data analysis using Spark.
• Skills: Proficient in more than one programming language among Python, R, Java, C++, or C. Hands-on experience with open-source tools for deep learning and machine learning technology such as Pytorch, Keras, Tensorflow, Scikit-learn, and Pandas.
• Bonus: Previous experience developing data-driven statistical models is desirable. Proficiency with PySpark for large data analysis is preferred.