High proficiency in statistical concepts and hands-on experience on Time Series Forecasting, Regression Techniques, clustering, ARIMA Predictive Modelling, State Space Modelling, Implementation of smoothening, RNN, LSTM, Classification, Logistic Regression, SVM, decision trees, Random forest and data filtering techniques (Non-Negotiable).
Exposure to programming languages (Python, Machine Learning, ML-Flow, SQL, etc) (Non-Negotiable)
Experience with Azure cloud platforms (Non-Negotiable)
Experience between from 5 to 12 years in Analytics industry (Negotiable)
Education Level: Preferably a PhD or Masters in Statistics or Operational research (Negotiable)
Working experience on advanced techniques like GBM and NLP (Negotiable)
Experience with Hive / Spark / Teradata and NOSQL databases (Negotiable)
Worked with at least one mainstream machine learning frameworks, including TensorFlow, PyTorch , ConvNet (Negotiable)
Primary Job Responsibilities:
Need hands-on candidate for building Data Science models and performing research.
Manage the continuous improvement of data science and machine learning led initiatives by following industry best practices and staying up to date with and extending the state-of-the-art in data science research.
Excellent academic or industrial track record of proposing, conducting, and reporting results of original research, plus collaborative research with publications.
Research for the identification new opportunities and work on build new POCs
Work closely with leadership in devising strategy for pioneering and implementing advanced analytics methods to identify and solve business problems in healthcare
Work closely on project from analytical design to implementation
Ensure timely and proper communication to Business Partners
Timely and quality delivery of engagements with minimum supervision
Work with the business teams to operationalize the analytics solution for problems in areas of Operational Analytics
Acquire understanding of US Healthcare domain and knowledge on Epidemiology