- Develop and apply quantitative and qualitative analytic methods to identify, collect, process and analyze large data sets for specified purposes.
- Develop predictive models that are scalable, repeatable, effective, and meet the expectations of the decision-makers and stakeholders.
- Serve as a cross-product expert, providing technical guidance in Machine Learning, Natural Language Processing, Data Mining and Information Retrieval experiments and projects.
- Analyze use cases, understand user behaviors, identify repetitive and/or error prone manual human processes that can be augmented or automated.
- Develop polished, high-impact persuasive reports and presentations that enable strategic decision-making supporting the project’s mission.
- Doctorate or Master’s Degree in Information Technology, Computer Science, quantitatively focused social sciences, or other quantitative fields.
- 5+ years of experience working with large and varying data sets, applying qualitative and quantitative analysis to interpret the data.
- 3+ year hands-on software engineering and Data Science experience working from data prep, modeling and feature engineering all the way through to deployment.
- Develop algorithms and predictive models to derive insights and business value from data.
- Test and validate algorithms and models using Machine Learning, Deep Learning and other modern techniques/methodologies.
- Demonstrable proficiency in coding Python, R, Java, Scala, C++, programming concepts, IDEs and big data frameworks (Spark, Hadoop).
- Strong knowledge and experience with several Data Science and ML/DL libraries or tools such as H2O, Spark MLlib, ML pipelines, Scikit-learn, H2O, Keras, Tensorflow etc.
- Hands on experience with Data Science notebooks such as Anaconda, Jupyter, and Zeppelin
- Strong understanding of machine learning methods such as classification, feature selection, clustering, neural networks, etc.
- Experience and proficiency in utilizing statistical/analytic packages such as SAS, R, SPSS, S-Plus, Matlab to develop statistical models
- Understanding of and experience with building canned and ad-hoc reports based on user requirements.
Key skill Required
- big data