Machine Learning Data Analyst

Ishita Reddy Annreddy

Baltimore, MD +1-443-***-**** ************@*****.*** LinkedIn GitHub

PROFESSIONAL SUMMARY

Detail-oriented and highly motivated Data Science Master’s graduate with progressive experience with 3+ years of experience across data annotation, ETL development, and advanced analytics. Proven track record of supporting machine learning initiatives, optimizing data pipelines, and delivering actionable business insights. Skilled in SQL, Python, AWS services, and building scalable dashboards and reporting tools using Tableau. Adept at improving data quality, automating workflows, and collaborating cross-functionally with engineering and business teams. Passionate about data reliability, operational efficiency, and translating complex datasets into strategic outcomes. EDUCATION

University of Maryland, Baltimore County August 2023 - May 2025 Master of Professional Studies - Data Science (GPA: 3.9) Sathyabama University, Chennai, India June 2019 – May 2023 Bachelor of Engineering - Computer Science (GPA: 8.87/10) SKILLS

• Programming: Python, SQL, R, Java, C++

• Databases: MySQL, AWD Redshift

• Cloud & Big Data Technologies: AWS (S3, Redshift, Glue, Lambda), Azure, Hadoop

• Data Visualization: Tableau, AWS QuickSight, Matplotlib, Seaborn

• Workflow Tools: Git, VS Code, Excel, Jupyter Notebook

• Core Competencies: ETL Development, Data Modeling, Pipeline Automation, Business Communication EXPERIENCE

Data Analyst August 2024 – Present

TD Bank, Albany, USA

• Analyzed large volumes of structured and unstructured data from internal systems and third-party platforms to uncover trends, generate insights, and support key business decisions across departments.

• Designed and automated interactive dashboards and reports using Tableau and Python, streamlining performance tracking and enabling real-time visibility.

• Collaborated with cross-functional teams to define KPIs, transform raw datasets, and build models that supported customer behavior analysis, retention strategies, and workflow optimization.

• Used SQL and Python for complex querying, data wrangling, and automation tasks, reducing manual reporting efforts by 30% and increasing reliability.

• Presented clear data stories with impactful visualizations and actionable recommendations, supporting strategic planning and executive-level reporting. Data Analyst June 2022 – May 2023

HCL Technologies, Chennai, India

• Implemented ETL pipelines using Python and SQL to consolidate sales data for real-time dashboards in Tableau, supporting data-driven business strategies.

• Ensured data lineage and quality by designing automated validation checks and resolving discrepancies through root cause analysis.

• Conducted root cause analysis on data discrepancies, ensuring high data reliability for downstream business intelligence use cases. Optimized SQL queries to improve data extraction performance by 20%, supporting a fast-paced business environment.

• Collaborated in an Agile environment with cross-functional teams to deliver scalable data solutions and improve stakeholder engagement. Data Associate March 2021 – May 2022

Quadratyx, Hyderabad, India

• Supported early-stage development of supervised ML models by preparing, validating, and annotating large-scale datasets (text, image, and speech).

• Worked closely with ML engineers to ensure data quality, relevance, and consistency across training datasets—laying the foundation for downstream analytics and model performance.

• Identified anomalies, defined edge cases, and provided feedback on labeling tools, improving annotation efficiency and accuracy by 15%.

• Contributed to internal QA and data documentation processes, gaining hands-on exposure to real-time ML pipelines and model evaluation frameworks.

• Developed a solid foundation in structured/unstructured data handling, driving a transition into more analytical and business-facing roles. PROJECTS

AI for Automated Radiology Report Generation

• Led data preprocessing and label binarization for 51,000+ chest X-ray images from the NIH ChestX-ray14 dataset.

• Developed a multi-label image classification model using an ensemble of EfficientNet-B3 and DenseNet121 CNNs, optimized for detecting 7 diseases.

• Achieved average AUROC of 0.83 and macro F1 score of 0.51 across 5 stratified validation folds.

• Deployed the model as an interactive web app using Gradio on Hugging Face, allowing users to upload X-rays and instantly receive an AI-generated diagnostic report in PDF format. Integrated CLAHE for contrast enhancement and applied real-time data augmentation for improved generalization.

• Collaborated in building the inference pipeline, batch processing logic, and report formatting using ReportLab for professional output. Healthcare Cost Disparity Analysis

• Modeled CMS data to detect geographic patterns in healthcare costs; built regression models (high R, low RMSE) and deployed recommendations to support operational equity initiatives.

• Simulated bundled payment and telehealth interventions to optimize healthcare access in rural areas.

• Visualized regional cost disparities using Tableau heatmaps, highlighting underserved zones with high treatment costs.

• Applied data governance principles to model structured CMS data, ensuring integrity and reproducibility for cross-state comparisons. Financial News Sentiment-Driven Stock Prediction

• Built an end-to-end pipeline from web scraping financial news using Beautiful Soup, sentiment analysis via NLTK, integration with historical stock price data, and machine learning modeling using Scikit-learn and TensorFlow.

• Achieved 85% prediction accuracy, enhancing real-time investment decision-making.

• Created interactive charts in Matplotlib and Seaborn to monitor sentiment trends aligned with stock volatility. CERTIFICATIONS

• AWS Certified Cloud Practitioner

• AWS Certified AI Practitioner

• Microsoft Certified – Azure AI Fundamentals

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