Responsibilities
•Entire machine learning/ predictive modelling implementation process: model design, feature planning, system infrastructure, production setup and monitoring, and release management.
•Extract and analyse the large volume of data to find the hidden insights.
•Design and implement highly scalable predictive models to perform various analyses like energy forecasting, anomalies detection, performance evaluation, risk analysis, preventive maintenance, proactive failure detection etc. in real time for distributed computing environments.
•Improve the accuracy of built predictive models by implementing complex machine learning/ statistical analyses algorithms.
•Test the developed models in distributed and non-distributed environment in the cloud.
•Migrate the developed models to satisfy different business requirement and work entirely in different environment.
•Being able to create examples, prototypes, and demonstrations to communicate findings clearly to both technical & non-technical audiences.
Requirements
•B.E/ BTech/ ME/MTech/MS/PhD in quantitative field such as Statistics/Mathematics/Computer Science or related field from premier institutes.
•Advanced knowledge of data mining/predictive analytics/statistical modelling/machine learning/artificial intelligence/operation research.
•2+ years of work experience with statistical analysis and machine learning techniques such as regression & classification, time series forecasting, decision trees,random forests models, artificial neural networks, support vector machine, clustering etc. for large data sets.
•Excellent knowledge of statistical analysis tool (such as R/ Mahout/ Weka) preferably R.
•Experience with big data platforms such as Hadoop, MapR, Pig, Hive, HBase etc.
•Proficiency in at least one Programming Language: Java,Python,Scala, Ruby, Perl.
•Knowledge of SQL databases, additionally at least one NoSQL database such as MongoDB, HBase and Cassandra would be advantageous.
•Familiar with cloud environments such as AWS, Rackspace and CtrlS.
•Understanding of UNIX / LINUX environment is plus.
•Familiarity with at least one versioning tools: Bitbucket, gitLab, SVN.
•Being able to work autonomously and enthusiastic to learn new technologies.