Sindhura J.

AI/ML Engineer @ Anthem, Inc.

About

With 8 years of experience as an innovative Data Scientist and AI/ML Engineer, I have designed, developed, and deployed machine learning and AI solutions across diverse industries including healthcare, insurance, and finance. My expertise spans supervised and unsupervised learning, deep learning architectures (CNNs, RNNs, Transformers), NLP, and anomaly detection, complemented by hands-on experience in recommender systems, computer vision, and time series forecasting. I am skilled in automating large-scale data pipelines using AWS Glue, Airflow, and Snowflake, and deploying scalable ML systems through MLOps frameworks incorporating Docker, CI/CD, and GitHub Actions. Proficient in SQL and distributed processing frameworks like PySpark, I have led end-to-end ML projects from data ingestion to production monitoring, while ensuring explainability through SHAP, LIME, and feature visualization. Known for my ability to translate business needs into actionable data-driven solutions, I collaborate closely with cross-functional teams and mentor junior talent, consistently delivering high-impact AI systems that enhance operational efficiency, drive strategic decision-making, and improve customer experiences.

Country

City

United States

Industry

Computer Software

Skill

Theano, Kubernetes, Hugging Face Products, LangChain, BERT (Language Model), NLTK, LightGBM, Docker Products, Tesseract, Microsoft Power BI, Flask, TensorFlow, Scikit-Learn, Hadoop, Yarn, Linux, Apache Flume, Spark SQL , Azure sql database, Azure Data Factory

Experience

AI/ML Engineer

Anthem, Inc.

2024-7 - Present · 2 yrs 3 mos

Developed and deployed AI/ML solutions including deep learning, NLP (BERT, ClinicalBERT), and anomaly detection models to enable fraud detection, patient risk forecasting, and claims optimization, while ensuring HIPAA compliance. Built scalable end-to-end MLOps pipelines using Docker, Airflow, MLflow, and AWS/Azure cloud services, integrating ETL workflows for structured and unstructured medical data with high data quality and governance. Collaborated with clinicians, data engineers, and DevOps teams to deliver predictive analytics, interactive dashboards, and automated support tools, driving clinical decision support and operational efficiency in healthcare systems.

Senior Data Scientist

New York Life

2022-4 - 2024-6 · 2 yrs 3 mos

Developed and deployed advanced machine learning and NLP models (including XGBoost, BERT, and anomaly detection) to improve underwriting accuracy, reduce fraud, and personalize insurance product recommendations. Automated large-scale data pipelines using AWS Glue, Snowflake, and SageMaker, increasing data refresh and model deployment efficiency while ensuring regulatory compliance through explainable AI techniques. Led cross-functional collaboration with actuaries, business analysts, and DevOps teams to deliver data-driven insights, optimize risk segmentation, and drive decision-making at the executive level.

Data Scientist

Ally

2019-12 - 2022-3 · 2 yrs 4 mos

Developed and deployed end-to-end machine learning solutions for credit risk scoring, churn prediction, and fraud detection, leveraging algorithms like XGBoost, Random Forest, and advanced NLP models. Automated ETL pipelines and model deployment workflows using AWS Glue, PySpark, Flask, and Docker, ensuring scalable, production-grade analytics across financial data systems. Built explainable AI dashboards and customer segmentation frameworks to support personalized marketing, regulatory compliance, and data-driven decision-making in banking operations.

Machine Learning Engineer

HDFC Life

2017-5 - 2019-9 · 2 yrs 5 mos

Developed and deployed machine learning models for churn prediction, fraud detection, customer segmentation, and cross-sell optimization in the insurance domain. Automated end-to-end data pipelines and integrated predictive insights into business workflows using Python, SQL, Flask APIs, and cloud platforms like AWS/Azure. Collaborated with actuarial, risk, and IT teams to embed ML solutions into underwriting and pricing systems, while building real-time dashboards and ensuring model governance compliance.