• A talented, self-motivated data engineer cum data scientist and technologist with significant experience in Advanced Machine Learning, Applied Mathematics, Social Network Analysis, Data Visualization, Knowledge Management, Statistical Analysis, Time Series Analysis, Deep Learning, Optimization, Operations Research, Text Analysis, Natural Language Processing, Digital Signal Processing, Recommendation Systems and Decision Sciences with a passion for turning raw data into products, actionable insights, and meaningful stories.
• Excels at cultivating, managing and leveraging relationships with end-users and has an ability to consider data from multiple systems, at different timescales, and in complex formats to discover hidden relationships and useful information.
• Progressive leadership experience in the areas of education management and Research/ energy informatics/health-care analysis/ risk analysis/ entertainment/ insurance industry/ media analysis/ green energy/intelligence markets / commercial analysis, commitment to purpose, and skills to finds order in chaos.
• Highly accurate and experienced Data Scientist adept at collecting, analyzing, and interpreting large datasets, developing new forecasting models and performing data management tasks. Possessing an extensive analytical skills, strong attention to detail, and significantly ability to work in team environments. Experience in working in:
Deep Learning Models: RNN,CNN,LSTM, GAN, BSTS, Prophet, AutoEncoders,
Boltzmann Machines, BERT, GPT3, etc.
Machine Learning Models: Linear Regression, Logistic Regression, Decision Tree,
Random Forest, SVM, GBM, XGBoost, KNN, PCA,etc.
Python: Pandas, Numpy, sklearn, Tensorflow, Keras, NLTK, Spacy, Gensim
R: dplyr, data.table, H2O, XGBoost, Caret, LightGBM, e1071, rpart, nnet,etc.
AWS: ML Engineering, DevOps, AWS Sagemaker/ Blazing Text, Databricks, CI/CD
- Current Company 2018 - Present
The designer and developer of Predictive *** platform at Current Company The platform is implemented and deployed where customers can find when will the next inspection be required and what will be the error rate. The *** platform enables customer for the -Prediction for the next inspection using machine learning/deep learning Other responsibilities are: -Build machine learning solutions and Proof of Concepts using the best combination of existing and open source technology and *** automotive predictive capabilities. -Leading technical demonstrations of *** both for technical and non-technical audiences. -Mastering technical tools for manipulation of data sets (R, Python). -Use data visualization techniques with open source libraries (R, Python). -Explore the data by searching for relationships, trends, anomalies, and outliers. -Modify the data by creating, selecting, and transforming the variables for model selection. -Model data by using analytical tools to train statistical/probabilistic models to reliably predict desired outcomes. -Develop mathematical models for detecting fault of Wind Turbine Blades.
- Gram&Juhl 2017 - 2018
Develop machine learning algorithms in Turbine Condition Monitoring System (TCM) in order to: • Identify the existence and location of damage in wind turbines • Identify the type and severity of damage in wind turbines • Modeling state of the art Condition Indicators • Development of new Condition Indicators • Prediction of Health Indicator for some components of Wind Turbines • Identify the existence and location of damage of the components • Prediction of Remaining Useful Life of the components of Wind Turbines/ the components (of the Wind Turbines) • Data Validation • Collaborating with the data engineering team on developing data pipelines • The prediction error and reliability must be improved to cope with the market requirements
- Presidents Institute 2017 - 2017
Responsible for: -Automatic lead generation -Lead scoring -Predictive analysis of customer behavior -Segmentation and Recommendation System