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Data Scientist Engineer

Data Scientist Engineer
by Admin on 06-15-2022 at 2:17 pm

Website TSMC

Job Responsibility: 

Ideal candidates would have either a Master’s degree or a Ph.D. in computer science, information systems, information science or related fields. The responsibilities of this role include developing machine learning, deep learning, text mining, and/or network science-based approaches to extract insights from structured (transactional) and unstructured data sources.


  • Excellent knowledge of Python (with Jupyter, PyCharm, or similar environment), GitHub, and Markdown
  • Extensive experience with data wrangling, data pre-processing (e.g., de-duplication, imputation), and data extraction from structured and unstructured (e.g., social media) data sources with tools such as Pandas or SideTable
  • Experience in developing classical machine learning (supervised or unsupervised) algorithm pipelines, including data annotation, feature extraction/weighting, model selection and tuning, and model evaluation using tools such as scikit-learn
  • Strong knowledge of fundamental statistical principles, including basic knowledge of probability, probability distributions, relationships between variables, hypothesis testing, and statistical significance
  • Experience with executing network science, including network construction, centrality measures, and community detection using packages such as networkX or iGraph
  • Experience with text analytics (e.g., named entity recognition, parts of speech tagging, parsing, coreference resolution, topic modelling, sentiment analysis) and text graphs using packages (e.g., NLTK, Spacy, or Gensim)
  • Fundamental and hands-on implementation knowledge of deep learning, including constructing data encodings (graph, tree, grid, sequence), basic processing units (e.g., ANN, RNN, CNN, GNN), model extensions (e.g., attention mechanisms, highway), learning paradigms (supervised, unsupervised, adversarial), and learning strategy and implementation (optimization, regularization, gradient descent, dropout) using packages such as Keras, PyTorch, or TensorFlow
  • Front-end/UI development and deployment using serverless technologies such as Streamlit and Netlify is a plus.
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