Product and IT Technology Pathfinder Internship - Data Science Master students welcome
TransUnion
- 香港
- 實習
- 全職
- Currently pursuing master’s degree in Computer Science, Data Science, Applied Mathematics, Statistics or a related field
- Proficiency in programming languages commonly used in Machine Learning, such as Python, R or Java, is essential
- Solid understanding of fundamental Machine Learning concepts, algorithms and techniques. Practical experience in applying Machine Learning to real-world problems is a plus
- Solid knowledge of Machine Learning lifecycle: data collection, annotation, modeling, evaluation, deployment & monitoring
- Good problem-solving and analytical skills, with the ability to think creatively and propose innovative solutions.
- Strong communication and teamwork abilities, with the willingness to contribute to a collaborative and fast-paced environment
- Attention to detail and an understanding of data ethics and privacy considerations
- Self-motivated and able to work independently, while also being open to learning from experienced team members
- Collaborate with the Product and Global Technology teams to understand the requirements and objectives of TU products and solutions
- Preprocess, clean, and analyze address data to identify patterns, anomalies, and potential areas for improvement
- Assist in the development, implementation, and testing of Machine Learning models and algorithms to enhance solutions capabilities
- Explore and experiment with different Machine Learning techniques, such as supervised learning, unsupervised learning, and deep learning, to optimize solution’s accuracy and efficiency
- Train and fine-tune Machine Learning models using diverse datasets
- Evaluate and validate the performance of Machine Learning models using appropriate metrics and cross-validation techniques
- Collaborate with the Global Technology team to integrate Machine Learning models into the TU solution, ensuring seamless functionality and performance.
- Monitor and analyze the performance of the machine learning algorithms in production, identify areas of improvement, and propose iterative enhancements