About the role
Literature review and understanding of working principle of Machines. Predictive modelling implementation process: model design, feature extraction, Model performance tuning, System Implementation and Deployment in Production environment.
Extract and analyse large volume of data to find insights based on inputs from in-house / External Domain Experts. Ability to compare and benchmark performance of various algorithms.
Design and implement highly scalable models to perform various analyses like Efficiency benchmarking, fault prediction, energy forecasting, anomaly detection, performance evaluation, risk analysis, preventive maintenance etc. in real time data.
Improve the accuracy of existing predictive models by implementing machine learning / statistical analyses algorithms
Being able to create examples, prototypes, and demonstrations to communicate findings clearly to both technical & non-technical audience
Requirements
Educational Qualifications
BTech/ME/MTech/MS/PhD in Mechanical / Chemical, currently working in Data Science / Python.
Relevant Experience:
3+ year of work experience with Statistical Analyses and Machine learning techniques such as Regression, Classification, Time Series, Random Forests, Artificial Neural Networks, Data Pre-processing, Clustering etc. for large data sets.
Strong understanding of mechanical engineering systems like Motors, Pumps, Compressor, Chillers, etc
Experience in development of production ready prediction systems working in real-time environment
Has solved engineering/reliability problems with machine learning
Desired skills/knowledge
Excellent knowledge of working with Python, Pandas, Visualization
Energy Domain knowledge would be a plus.
Knowledge of RESTFul APIs, SQL would be a plus
Understanding of UNIX / LINUX environment is plus
*Applicant without an attached CV will not be shortlisted*