Data Scientist - R/Statistical Modelling

Requirements

- Education - PhD/ Master's degree in Data Science, Applied Statistics, Applied Mathematics, Operations Research or equivalent strongly preferred; MS/PhD in engineering with emphasis on applied statistics or statistical modeling or MS/PhD in Computer Science or other quantitative disciplines with emphasis in machine learning and database management in unstructured data environment (Hadoop) also considered.

- Statistical programming skills: At least two years- experience applying scientific methods in the field of analytics using statistical and mathematical programming packages such as R, Python. Java, Perl, C/C++ or any other programming language is a plus.

- Machine learning - Expertise in supervised and unsupervised learning using a variety of machine learning algorithms is a must. Candidate should have both theoretical and practical understanding of parametric and non-parametric classification and regression models. Experience analyzing and presenting complex data and proven problem solving abilities is desired.

- Feature engineering and Time-Series data analysis - Candidate should showcase expertise in engineering new features to improve model prediction/inference. Experience with time-series data is highly desired.

- Hadoop Platform - Experience with Hadoop, Spark, and Cloud computing ecosystem/platform will be preferred. Experience with Hive, Pig, Scala and MapReduce is desired. Parallel computing experience with Spark is highly desired.

- SQL Database/Coding - Candidate should be able to write and execute complex queries in SQL.

- Visualization software - Advanced skills with TIBCO Spotfire or other visualization platforms are desired.

- Proficient in using data analytics tools to perform queries and analyses and for defining and correlating data, and skilled at utilizing data visualization platforms to organize and present summarizations, predictive analysis, comparative analysis, dashboards and reporting.

- Perform data mining and analytics to support ongoing continuous risk monitoring and risk assessments of operational data to recognize patterns and trends, investigate anomalies, and assess internal control environment.

- Experience with the design and implementation of data governance processes; the assessment and remediation of data quality related issues; working with large and complex enterprise data models and large volumes of data.

Note : My client is looking for excellent stability, excellent communication & potential candidates only