- Use data visualization, statistical analysis, pattern recognition, and machine learning – along with domain knowledge and subject-specific models – to solve science, engineering, and commercial problems.
- Data extraction/transformation/loading, data filtering, and quality control, exploratory data analysis, feature engineering, and visualization. Ability to work with both structured and unstructured data.
- Analyze results and develop technical reports and presentations.
Qualification & Experience:
- Currently pursuing an MS (exceptional BS candidates will be considered) in one of the following disciplines: Engineering/Sciences, Mathematics, Statistics, or Computer Science with experience in data analytics.
- Proficient in MS SQL Server and Python
- The following experiences are all considered an advantage, but not required: Using one or more of the following software packages: sci-kit-learn, NumPy, pandas, Jupiter, matplotlib, scipy, nltk, spacy, Keras, TensorFlow.
- Solving problems using techniques including Regression, Support Vector Machines, Decision trees, random forest, Boosting, PCA, KMeans.
- Using SQL/No SQL databases is an advantage