Position: Data Scientist
Requirement is for an individual with analytical & programming background, with applied Data Science experience, curiosity, and passion towards Big Data technology, to serve as a Lead Data Scientist for our Information Technology Unit. Judiciary is working in the areas of NLP (Natural Language Processing), Text Mining, and Deep Learning, to solve business problems and build predictive analytics products.
- 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.
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