Data Scientist & Machine Learning Engineer
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Dear Hiring Manager,
As a highly skilled Machine Learning Engineer and Data Scientist with five years of industry experience, I am excited to express my interest in the available position at your esteemed organization. Your organization has a reputation for being a leader in the industry, and I believe that my skills and experience align perfectly with the requirements of the position.
Currently I am with IBM, Where I am designing and developing a new loss function based on the KNN algorithm, which results in a remarkable 12% improvement in the accuracy of the systems. Implementing Deep Learning models to work accurately on unbalanced data using NumPy and Pandas library
I am confident that my knowledge and experience make me an ideal candidate for this position, and I am excited to have the opportunity to contribute to your organization's growth. I would appreciate the chance to discuss my background and expertise with you further, and I am available for an interview at your earliest convenience.
Thank you for considering my application. I look forward to hearing from you soon.
Sincerely,
Designed and developed a new loss function based on the KNN algorithm to improve the accuracy of systems by 12% .Implemented Deep Learning models to work accurately on unbalanced data using NumPy and Pandas library. Designed and developed a customized compact network for reducing computational time and memory. Developed an optimized coordinate research algorithm to address an expensive processing time problem. Provided agencies with some insights into the costs of treating various diseases. This is done while smoothing the data and ignoring any trivial data. Implemented new knowledge distillation method and SVM on real-world data to improve the performance of the model. Designed multivariate time series forecasting models using LSTM, GRU, CNN, and Transformers for IoT devices in the TensorFlow framework with an accuracy of 83%. Built automated data ingestion, data cleaning, feature selection, preprocessing, denoising, hyperparameters tuning, and model training pipelines for large-scale and real-time data. Performed deep-dive analysis of data from different business applications and drive strategic decisions. Translated business problems into analytical structures and solved them using statistical/ML techniques. Used modern machine learning techniques such as classification, clustering, and time series forecasting techniques as per the requirement in the project. Involved in writing code in Python/R and building ML packages when required.
Developed a system to calculate the optimal vehicle route which optimized the resources used in the supply chain. It reduced transport costs by 8% Designed, evaluated, and built the backend infrastructure to manage the machine learning model lifecycle by working with data storage technologies such as AWS Data Lake and AWS S3 cloud storage, APIs for incoming model predictions, Airflow for retraining pipelines, MLflow for model tracking, and Red Hat’s OpenShift for container orchestration allowing for solution deployment. Worked on diverse Data Science and ML domains –Supervised & Unsupervised Learning, Time Series Forecasting, Predictive Modeling, Text Analytics, Probabilistic Graphical Models, Bayesian Belief Networks, Marketing Mix Problems, Growth Drivers Analysis, and Digital Attribution. Improved the customer experience by providing the customer information upfront to the service executive by performing ANPR (Automatic Number Plate Recognition) on the Car when it arrives in a car showroom. Generated data annotation scripts from scratch and convert the data into yolo format and Pascal VOC and created the TF-Records. Investigated, designed & implemented a library deprecation detection tool by applying NLP-based sentiment analysis on GitHub & Stack Overflow Q&As. Implemented using K means clustering algorithm in R by Merging and transforming datasets into a single dataset and K means clustering for identifying the group of businesses having similar business trends in the group, finding association for the group over a period to mark them as potential competitors.
