Achyutha Raju Sandesi

AI Data Analyst with a Master's in IT and 4+ years of experience applying machine learning and statistical analysis to healthcare operations and diagnostic technology. Transforms complex claims, clinical, and imaging data into predictive models and dashboards that directly reduce expenditures and improve patient risk stratification. Delivers data-backed strategies to cross-functional teams by bridging the gap between raw data engineering and executive decision-making.

AI Data Analyst - Centene Corporation - St. Louis, MO

(2024-07)

• Architected predictive models (CMS-HCC) using Python (XGBoost) and Spark to forecast member utilization and risk scores, decreasing unnecessary inpatient admissions by 12% over two consecutive quarters.
• Engineered scalable ETL pipelines on AWS (Glue, Lambda) to ingest and normalize 5M+ rows of monthly claims and pharmacy data, compressing processing time by 40%.
• Designed interactive Power BI dashboards monitoring HEDIS quality measures and member engagement KPIs, enabling clinical teams to close care gaps for 3,500+ high-risk members.
• Deployed Natural Language Processing (NLP) algorithms (AWS Comprehend) on unstructured clinical notes, automating diagnostic code extraction and saving 15 hours of manual chart review weekly.
• Designed and analyzed A/B testing frameworks for member outreach campaigns, identifying high-impact communication channels that improved program response rates by 18%.
• Standardized data validation frameworks ensuring PHI compliance and data integrity, reducing data quality incidents by 25% across the analytics department.
• Translated business requirements from actuarial and product teams into technical specifications, delivering roadmaps aligned with quarterly strategic goals.

Data Analyst - SigTuple - India

(2021-08 - 2023-12)

• Pre-processed 50,000+ medical images (microscopy, fundus) using OpenCV and Python, standardizing input for CNN training and enhancing model accuracy by 15%.
• Tuned hyperparameters for YOLO and ResNet architectures for anomaly detection in diagnostic scans, achieving a 92% sensitivity rate against clinical benchmarks.
• Managed PostgreSQL databases containing patient records and diagnostic metadata, executing complex queries to extract aggregated datasets for R&D validation.
• Performed hypothesis testing (t-tests, ANOVA) comparing AI diagnostic outputs against clinician ground-truth, generating statistical reports for regulatory submission.
• Orchestrated Apache Airflow DAGs to automate imaging data flow from hospital PACS systems into the company's data lake.
• Collaborated with medical professionals to design annotation protocols, reducing inter-annotator bias and improving dataset quality by 30%.
• Investigated correlations between patient demographics and disease prevalence using Seaborn and Matplotlib, uncovering trends that informed new model feature sets.
• Presented model performance metrics and data trends to clinical and product stakeholders, translating complex statistical findings to support product roadmap decisions.

Master of Science - Information Technology - Southern New Hampshire University

Bachelor of Engineering - Computer Science - QIS College of Engineering and Technology