Amulya Chebrole

Automated PDF processing for extracting various fields including reporter, patient, drugs, and lab tests, resulting in 10 times cost savings and an 80% reduction in human effort. Implemented the solution in real-time utilizing advanced Natural Language Processing (NLP) techniques and leveraging pre-trained models. Collaborated with clients to understand their business needs and requirements, providing tailored data-driven solutions to address complex challenges. Supervised and mentored a junior team member, providing guidance and support to facilitate their professional growth and development. Coordinated with the testing team to ensure thorough testing and validation of data analysis solutions, maintaining high standards of quality and accuracy. Contributed to cross-functional collaboration efforts, working closely with teams across departments to deliver integrated solutions and achieve organizational goals.

Developed APIs to streamline data access and facilitate seamless integration of analytical solutions into existing systems, demonstrating proficiency in Python and FastAPI. Expanded Python expertise to an advanced level, utilizing it effectively for data manipulation, analysis, and model development. Conducted a Proof of Concept (POC) on image recognition, exploring the feasibility of utilizing advanced computer vision techniques for business applications. Collaborated closely with the digital marketing team to leverage data-driven insights and analytics, enabling informed decision-making and optimizing marketing strategies. Prepared and presented daily project reports to senior management, providing updates on project progress, milestones achieved, and potential challenges. Analyzed data and generated actionable insights through graphs and visualizations, supporting strategic decision-making processes and driving business growth.

Trained various Machine Learning algorithms such as Linear Regression, Logistic Regression, Decision Tree, Random Forest, Adaptive Boosting, Gradient Boosting, XG Boosting, Naïve Bayes, Support Vector Machine, KNN. Performed Data Understanding, Data Cleaning, Data Visualization, Feature Engineering, Feature Scaling using Python libraries such as Pandas, Numpy, Sklearn, Matplotlib, Seaborn, Plotly. Expertized in designing and training deep learning (DL) architectures. Comfortable with tools/libraries/APIs such as NLTK, Testblob, Spacy, RNN, LSTM, Hugging Face, Google, OpenAI, Facebook etc. Experienced in deep learning frameworks such as TensorFlow/Keras.

Following upon missing digital materials, preparing reports on status, coverage, volume and such other metrics and preparing documents for internal management, external contacts, clients and assist in improving & developing new processes. Quality Control, Validation, Investigating & Resolving issues.