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Data Science with 2+ years in Python & Machine Learning
Operations Associate with experience in international student support, recruitment coordination, and operational management. Skilled in sourcing and identifying suitable US job opportunities for international candidates. Experienced in resume optimization and managing end-to-end job application processes to improve placement success. Proficient in recruitment tracking, reporting, and process coordination with strong attention to detail. Expertise in handling accurate and timely application submissions while ensuring smooth candidate support services. Strong communication and client coordination skills with the ability to work effectively in fast-paced environments. Experience in conducting market research and supporting strategic initiatives for international student services. Worked on website creation and service visibility enhancement to improve user accessibility and engagement. Knowledgeable in Python, SQL, Machine Learning, and NLP concepts with hands-on project experience in Medical Journal Curation (NER). Certified in Data Science using Python with a strong interest in operations, recruitment support, and client collaboration in US staffing services.
SRI MITTAPALLY COLLEGE OF ENGINEERING
B.Tech · ECE
August 1, 2018 – June 30, 2022
SQUIRREL GROUP
OPERATIONS ASSOCIATE
March 1, 2023 – August 31, 2024
India
360 DIgITMG
Intern
September 1, 2022 – February 28, 2023
Hyderābād, Telangana, India
Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence
September 1, 2022 – February 28, 2023
Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f-score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19's demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.
NLP project of Medical Journal Curation - Named Entity Recognition (NER) for Medical Journal
September 1, 2022 – February 28, 2023
Our client was confronted with the challenge of accurately identifying and classifying medical entities such as diseases, symptoms, drugs, and treatments from vast amounts of unstructured medical text. This hurdle was significantly impacting the accuracy and efficiency of their medical curation process. To overcome this challenge, we developed a robust Named Entity Recognition (NER) model specifically designed for medical text using cutting-edge technologies. Leveraging Python, Natural Language Processing (NLP), spaCy, data preprocessing techniques, model selection, evaluation methodologies, data visualization, and Flask, we successfully delivered an outstanding solution. Our NER model achieved an impressive accuracy rate of 80%, effectively identifying and classifying various medical entities crucial for supporting the medical curation process. Moreover, we implemented feature engineering techniques to optimize the model's performance and offered valuable recommendations for data collection and preprocessing strategies. Improved medical curation process with 30% fewer errors and 50% faster processing time. Developed a high-performing NER model for medical text with an accuracy rate of 80%.
The Appointment Booking System
September 1, 2022 – February 28, 2023
Developed an Appointment Booking System that enables patients to seamlessly schedule doctor appointments online, improving accessibility and user convenience. Designed and implemented responsive frontend interfaces and robust backend services to streamline appointment management and enhance overall system efficiency. Integrated secure user authentication, appointment scheduling, and real-time data handling to ensure a smooth and reliable healthcare booking experience. Built RESTful APIs and optimized database interactions for efficient appointment tracking, patient management, and system scalability while collaborating in an Agile development environment for feature enhancements, testing, debugging, and deployment activities.
DATA SCIENCE USING PYTHON
360DIGITMG
January 1, 2022 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying data science to real-world problems, particularly in the medical domain (NER for medical journals, COVID-19 detection). This indicates a problem-solving mindset and a drive for impactful work. The 'Operations Associate' role, while not technical, suggests experience in process coordination and client interaction, which can be valuable in cross-functional data science teams. However, the overall experience level (2 years) and the nature of the roles (internship, operations associate) suggest a junior profile rather than a senior one, which might impact cultural fit for a senior Data Science role requiring significant independent contribution and leadership.
Soft Skills & Operational Fit
The candidate's professional summary highlights strong communication and client coordination skills, experience in fast-paced environments, and collaboration in Agile development. These indicate a potential for good operational fit and teamwork. However, the primary work experience as an 'Operations Associate' and 'Intern' does not directly align with a senior Data Science role, suggesting a gap in practical, industry-level data science operational experience.