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Data Scientist/machine Learning Engineer Resume

Santa Clara, CA


  • Data Scientist/Machine Learning Engineer with 10+ years of industry experience
  • Develop and deploy machine learning models based on Artificial Neural Networks, Principal Components Analysis, Random Forest, Gradient Boosti ng, Partial Least Squares, k - means Clustering. Applications include Time Series Forecasting, anomaly detection, computer vision, sequence modeling, topic classification, sentiment analysis, Recommendation Engines, real-time process control/optimization.


Programming Languages/Packages: Python, R, Matlab, Keras, TensorFlow, Scikit, Numpy, Pandas, Matplotlib and Seaborn, Natural Language Tool Kit, Apache Spark, Hive, NoSQL


Data Scientist/Machine Learning Engineer

Confidential, Santa Clara, CA


  • Object identification and logo recognition in household images (AWS Marketplace model packages: GluonCV YOLOv3 and Sensifai)
  • Automated parsing of PDF forms to extract key-value pairs/Named Entity Recognition (AWS Textract, Spacy)
  • Call volume forecasting for customer service center (LSTM Sequence to sequence); Monitor news stories and associated sentiment to predict impact of external events (data breaches, natural calamities, etc.) on call volume to optimize call routing, resource scheduling and prepare response strategy
  • Topic clustering/classification and sentiment analysis to summarize and obtain actionable insights into customer complaints/reviews (LDA2Vec, Natural Language Toolkit, Spacy)
  • Real-time anomalous topic detection based on call/chat transcripts (LSTM)
  • Recommendation engine for prediction of customer problems and possible solutions based on customer’s geo-demographic data and interaction history (Random Forest, Gradient Boosting)

Applications Engineer

Confidential, Methuen, MA


  • Convolutional Neural Network model to predict the physiochemical properties of gasoline from hyper-spectral images
  • Deep Reinforcement Learning to determine optimal control trajectory in closed loop sequential control for cost minimization
  • Design of Experiments based on Latin Hypercube sampling to generate data and train a simulated model of the environment for Reinforcement Learning model
  • Hyper-parameter search using Bayesian optimization
  • Significant contribution towards the growth/success of a successful startup company (revenue realization to increase in revenue up before acquisition by Confidential )

Applications Scientist

Confidential, Woburn, MA


  • Principal Components Regression and Partial Least Squares Regression to predict physical properties of oil and natural gas samples in real-time (PCR, PLSR)
  • Confidential model for high-altitude aerial monitoring to detect and quantify natural gas pipeline leaks using images captured by a LIDAR/Tunable Diode Laser
  • Weibull distribution fitting and Cox regression modeling of data collected from a ccelerated life tests, warranty/failure data (Reliability analysis, S urvival/Hazard R ate F unction)
  • Interpret statistical methodologies and results to non-statisticians in a multidisciplinary research team/environment

Research Assistant

Confidential, Raleigh, NC


  • Random Forest based model to classify gasoline samples into different octane ratings with high accuracy for compliance and reporting at retail sellers/distributors
  • C lustering based sampling technique to address between-class and within-class imbalance to improve classification performance of Random Forest models in a highly imbalanced multi-class classification problem
  • Cleaned and analyzed large and complex data sets
  • Review, implement and modify statistical algorithms described in research papers or available under open source

Associate Software Engineer



  • Write and execute test case scenarios/scripts to validate business requirements for Siebel based customer relationship management application
  • W rite test scripts for quality assurance of web based financial applications

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