Understand client’s business problem space in its full generality and define an appropriate model representation (ontology,topology) that does justice to the inherent incompleteness, uncertainty and fuzziness in both the data and the underlying real-world entities.
Identify and classify recurring types of problems, and the most effective techniques for each of them (e.g. clustering, dimensionality reduction, classification, NLP techniques, etc.)
Architect Analytic solutions utilizing Big Data Technologies as Hadoop Technology stack (Map Reduce, Hive, Pig, Mahout, Spark, etc.).etc.
Fine-tune/customize algorithms for specific applications based on business requirements and available data
Continuously enhance and optimize algorithms based on analysis of their live performance and actual data
Discover the value in data flowing through to conceptualize data products to deliver business value for customers.
Individual in this role is expected to work with very large data sets (both structured and unstructured), data cleansing/transformation, feature engineering, apply statistical analysis, data visualization, data mining, design and develop algorithms and define path to implementation with product engineering teams
The successful candidate should possess strong advanced analytical techniques, technological and problem solving skills in solving real-world problems.
Should have strong experience in implementing advanced analytics approaches including statistical modelling, machine learning algorithms, text mining, etc. to answer business questions and drive actionable insights.
Should be able to decide on the best modeling/algorithm approaches and productize analytical solutions.
Ability to distill complex Big Data and technological concepts into intuitive business approaches.
Experience developing production systems for processing large volumes of structured and unstructured data in Java, Python or similar technology stack.
Proficiency in working with applications such as SQL, No-Sql , Hadoop, Hive, Pig,etc. and statistical applications such as SAS, SPSS, R, Revolution R, Python or similar open source statistical applications with a focus on Machine Learning