Senior Data Scientist at Baylor Scott and White Health
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* Passionate about healthcare and striving to innovate better solutions to solve the complex healthcare problems using advanced technologies and artificial intelligence * Keen on building machine learning models and deploying them on cloud for helping the physicians take real-time decision about their patients
Southern Methodist University
Master of Science (M.S.), Electrical and Electronics Engineering
January 1, 2015 – May 1, 2017
Baylor Scott & White Health
Senior Data Scientist
April 1, 2023 – Present
Dallas-Fort Worth Metroplex · Remote
PCCI
Manager, Machine Learning Engineering
October 1, 2020 – February 1, 2023
PCCI
Machine Learning Engineer
February 1, 2019 – September 1, 2020
PCCI
Data and Applied Scientist
May 1, 2018 – January 1, 2019
UT Southwestern Medical Center
Data Scientist
June 1, 2017 – May 1, 2018
Dallas/Fort Worth Area
UT Southwestern Medical Center
Research Assistant-Machine Learning
May 1, 2016 – June 1, 2017
Dallas/Fort Worth Area
Technophilia Systems pvt.ltd
Technical Associate
June 1, 2014 – July 1, 2015
Deep Learning Based Facial Recognition Challenge by Kaggle (Tensorflow , Keras , AWS)
March 1, 2017 – May 1, 2017
Built a convolution neural net model using alexnet architecture for classifying facial expressions Configured an AWS ec2 cloud instance to train models and utilized T-sne algorithms for dimensionality reduction boosting the model’s accuracy up to 84%
Twitter Sentiment Analysis using NLTK
February 1, 2017 – April 1, 2017
Used NLP dependencies of Twitter like tweepy and textblob and performed sentimental analysis on various topics on Live Twitter Online Database and categorized positive and negative responses using polarity tests on sentiments.
Natural Language Processing with Deep Learning (NLTK , Tensorflow)
January 1, 2017 – March 1, 2017
Built a sentiment analysis classifier to predict sentiments of user reviews using recurrent neural network Performed data cleaning and data wrangling operations for smooth data ingestion to the NLP deep learning algorithm
Colored Object Detection in Images (OpenCV, Python)
July 1, 2016 – December 1, 2016
Developed and implemented a colored object detection algorithm with color invariance for identifying target objects in a cluttered image The algorithm first learnt the object features using HOG features and MSER features Detected object was marked by a bounding box in the cluttered scene
Machine Learning Based Classifier To Predict Successful Memory (Tensorflow , Python)
May 1, 2016 – Present
Improved the existing approach of logistic regression and attained an accuracy of 82% Deployed support vector machines and recurrent neural networks with LSTM to predict successful memory encoding Performed A/B testing to deploy the best classification model
Machine Learning Based Brain Tumor and Non Tumor Classification
February 1, 2016 – May 1, 2016
‘In this project, brain tumors are detected using a machine learning algorithm. The MRI image dataset were tested on various classifiers: k-Nearest Neighbors, Support Vector Machines, and Logistic Regression. Thus, images were classified into tumor and non –tumor images using these classifiers. The dependencies were sci-kit learn and NumPy.
AUTOMATIC TUMOR SEGMENTATION ALGORITHM
February 1, 2016 – May 1, 2016
'In this project, I made an automatic brain tumor image segmentation algorithm using adaptive segementation algorithm in Image Processing. This algorithm was translation and rotational invariant and it gave an accuracy of 95%.
Scala and Spark for Big Data and Machine Learning
Udemy
June 24, 2026 – Present
Complete Python Bootcamp
Udemy
June 24, 2026 – Present
Python for Machine Learning Bootcamp
Udemy
June 24, 2026 – Present
CLOUD COMPUTING WITH AWS
Udemy
June 24, 2026 – Present
Practical Deep Learning Using TensorFlow/Theano
Udemy
June 24, 2026 – Present
Data Science : Deep Learning in Python
Udemy
June 24, 2026 – Present
WEB-IR70 - FLIR C2
Flir
June 24, 2026 – Present
DEEP LEARNING : CONVOLUTION NEURAL NETWORKS USING TENSORFLOW
Udemy
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio and work experience are heavily skewed towards Machine Learning, Deep Learning, and Data Science, particularly in the medical domain. While these skills are valuable, the target role is 'Data Analyst'. This indicates a potential mismatch in the primary focus, as a Data Analyst role typically emphasizes data extraction, transformation, visualization, and reporting, rather than advanced model development and deployment. The breadth of skills is strong within ML/DL, but less explicitly demonstrated for core data analysis tasks. The project diversity is strong within the ML/DL domain but lacks explicit projects focused on traditional data analysis, dashboarding, or business intelligence.
Soft Skills & Operational Fit
The candidate's experience as a Manager, Machine Learning Engineering, and mentoring roles suggests leadership, project management, and team collaboration skills. The project descriptions indicate problem-solving and analytical thinking. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and direct communication clarity is limited.