Staff Engineer Data Scientist
Summary
Build and maintain large-scale data pipelines and deep learning models to drive AI-driven insights for semiconductor and green-energy solutions.
We are seeking a highly skilled Staff Engineer Data Scientist to grow our organization's data analytics and AI capabilities. The successful candidate will develop and maintain large-scale data pipelines, perform exploratory and predictive analysis, build deep learning algorithms, and maintain model quality. They will also communicate complex technical concepts to stakeholders and document analytical findings. Your Role Key responsibilities in your new role
- Growing the Organization's data analytics and AI capability
- Develop and maintain a data pipeline for large-scale data indexing.
- Engage in both exploratory analysis and predictive models to identify data trends and anomalies.
- Explore new hypotheses, build deep learning algorithms and be responsible to maintain model quality over time.
- Take ownership of the algorithmic structure and explain complex deep learning algorithms in layman terms to business stakeholders or tech talks.
- Ability to set and achieve project objectives & milestones.
- Document analytical findingsfor technical teams, executives, or publication
- Ph.D. or Masters in Natural Science, Computer Science, Data Science, Statistics, Mathematics, or equivalent fields.
- At least 3-5 years of relevant working experience in similar fields.
- Proficient in Python and SQL languages.
- Ability to handle text manipulation tasks such as processing and parsing.
- Understanding of recommender systems such as collaborative filtering and content filtering.
- Experience in using machine learning libraries such as Scikit-learn, TensorFlow, Keras or Pytorch
- Experience in model hyper-parameter tuning, embeddings and feature engineering.
- Experience in natural language process (NLP), statistical modeling, network analysis, and data mining techniques
- Love minimal, beautiful code and neat documentation.
- Team player, both internal between data scientist as external with business stakeholders
- Plus: Experience with cloud computing (AWS/GCP/Azure)
- Plus: Deep Learning (RNN, LTSM, XGBoost)
- Plus: Continuous deployment (CI/CD)