For more than 75 years, TE have partnered with customers to produce connectors and sensors with high reliability and durability that make a connected world possible. We provide solutions that power electric vehicles, aircraft, digital factories, and smart homes. Innovations in TE enable life-saving medical care, sustainable communities, efficient utility networks, and the global communications infrastructure.
The Singapore R&D Center is chartered to work with the CTOs of TE's Business Units to identify technical areas of interest for R&D and develop the technologies that will impact all segments. In order to improve the New Product Development process, TE is investing in upfront simulation, modelling and data mining capabilities, as a part of our Digitalization strategy. Effective use of modelling helps to get it right the first time. The intern will work under the supervision and mentorship of TE scientists to deploy data science in development of new product, material and manufacturing process.
Duties & Responsibilities:
- Assist in developing data science solutions to support new AI tools development in TE, to ensure data quality through rigorous validation and automated checks. Engineer predictive features from structured, semi structured, and unstructured datasets from experimental and simulation data.
- Apply cutting-edge data science models/algorithms to solve practical engineering/manufacturing problems, from Exploratory Data Analysis (EDA) to uncover patterns & anomalies, to design, train, and evaluate supervised & unsupervised models (regression, classification, clustering).
- Develop multiple vision/tabular data analysis algorithms powered by machine/deep learning to work on data sets
- Work in a multi-disciplinary environment with specialists in data science, material science, injection molding, metal stamping, and additive manufacturing fields
Qualifications:
Required:
- Currently pursuing Major in Data Science/Computer Engineering/Information System/Enterprise AI, Statistics, Mathematics, Business Analytics or related fields (Undergraduate or Postgraduate)
- Proficiency in Python (Pandas, NumPy, SciPy, scikit learn), SQL, and version control Gitlab/GitHub or similar platform.
- Good presentation and communication skills
- A self-motivated learner; A good team player
Preferred:
- Familiarity with machine learning algorithms, with knowledge of deep learning libraries (PyTorch, TensorFlow), probabilistic programming, and graph analytics
- Hands-on experiences with computer vision, tabular data analysis,
- Solid grounding in statistical inference, hypothesis testing, and experimental design.
