Available to hire
I am a data scientist with 3+ years of experience in developing and deploying AI/ML models, advanced analytics solutions, and data pipelines across healthcare and life sciences. I enjoy turning complex data into actionable insights and building scalable, production-ready solutions that support better patient outcomes and more efficient operations.
I am proficient in Python, R, SQL, and modern ML frameworks, and I routinely work with cloud platforms (Azure, AWS, GCP) to deploy models at scale while prioritizing data privacy and regulatory compliance (HIPAA, GDPR, SOX).
Skills
Language
English
Fluent
Work Experience
Data Scientist (AI/ML) at JPMorgan Chase
October 1, 2024 - PresentDesigned, trained, and deployed supervised ML models (Scikit-learn, XGBoost, TensorFlow) to predict credit default and customer churn, reducing default risk by 15% and improving retention strategies. Engineered deep learning architectures (CNNs, RNNs, Transformers) in PyTorch for fraud detection, boosting detection accuracy by 25% and reducing false positives by 15%. Built ETL/data transformation pipelines using Databricks, PySpark, and Airflow across 50M+ records, reducing data latency by 40% and enabling real-time risk analytics. Developed NLP models (BERT, SpaCy, Hugging Face) to extract insights from unstructured financial documents and communications. Integrated models into Snowflake and AWS SageMaker for real-time scoring. Established MLOps pipelines (MLflow, Docker, Kubernetes) for automated retraining and monitoring, mitigating drift. Created Tableau/Power BI dashboards backed by Snowflake datasets to track portfolio risk, fraud incidents, and customer analytics. Conducted A/B
Data Scientist (AI/ML) at Genpact
January 1, 2021 - July 1, 2023Designed predictive modeling solutions (Python, R) to forecast customer churn, credit risk, and operational inefficiencies, improving prediction accuracy by 22%. Developed PySpark pipelines in Databricks for preprocessing and integrating structured/unstructured data from ERP/CRM, reducing ETL time from 8 hours to under 2 hours. Implemented NLP models (Hugging Face, SpaCy, BERT) to extract insights from invoices, contracts, and customer feedback, increasing automation by 25%. Collaborated with data engineers to integrate models into Snowflake and AWS Redshift, enabling real-time dashboards and predictive risk scores. Automated end-to-end ML workflows with DBT, Airflow, and MLflow, improving experimentation throughput and reproducibility by 30%. Built Power BI dashboards connected to Snowflake; visualized risk scores and KPIs. Containerized and deployed models with Docker and Jenkins CI/CD; deployment cycles reduced by 35%. Deployed and monitored models on AWS SageMaker (EC2/Lambda) for
Education
Master of Science in Computer and Information Sciences at University of North Texas
August 1, 2023 - May 1, 2025Bachelor of Technology in Information Technology at Gayatri Vidya Parishad College of Engineering (A)
August 1, 2019 - May 1, 2023Qualifications
Industry Experience
Healthcare, Life Sciences, Financial Services, Professional Services
Skills
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