Chandu Reddy AI/ML Engineer and Data Scientist

AI-ML/Data Scientist

AmerisourceBergen

Jun 2025 - Current (1 year 3 months)

• Designed and implemented end-to-end machine learning and data science pipelines, transforming raw data into production-ready models that support business decision-making and automation. • Built and deployed advanced predictive analytics solutions for classification, regression, and forecasting problems, enabling accurate predictions and improved operational efficiency across business functions. • Developed robust data preprocessing and feature engineering frameworks to handle missing values, outliers, and high-dimensional datasets, significantly improving model performance and stability. • Applied ensemble learning techniques and hybrid modeling approaches to improve predictive accuracy, reduce variance, and enhance generalization across

AI/ML Engineer

UBS

Mar 2023 - May 2025 (2 years 3 months)

• Designed modular machine learning architectures to support reusable components for data preprocessing, feature engineering, model training, and inference across multiple projects and domains. • Developed intelligent predictive systems for business-critical use cases such as customer churn prediction, demand forecasting, fraud detection, and risk scoring, improving decision-making accuracy. • Built automated machine learning workflows (MLOps pipelines) enabling continuous integration, continuous delivery, and continuous monitoring of models in production environments. • Implemented advanced ensemble and hybrid modeling techniques, combining multiple algorithms to improve accuracy, robustness, and generalization of machine learning systems.

ML Engineer

Home Depot

Oct 2021 - Feb 2023 (1 year 5 months)

• Developed automated machine learning pipelines (MLOps workflows) enabling continuous training, validation, deployment, and monitoring of models with minimal manual intervention. • Built intelligent predictive systems for business-critical applications such as churn prediction, fraud detection, demand forecasting, and risk scoring, improving decision-making accuracy. • Engineered real-time machine learning inference systems using REST APIs and microservices architecture to support low-latency predictions in high-traffic production systems. • Implemented advanced ensemble learning techniques, combining multiple models to improve prediction accuracy, stability, and generalization across complex datasets. • Designed robust feature engineering

ML/Data Scientist

DXC Technology

May 2019 - Sep 2021 (2 years 5 months)

• Engineered robust feature engineering frameworks to transform raw data into high-quality features, improving model performance, stability, and generalization across datasets. • Implemented real-time and batch prediction systems using REST APIs and cloud infrastructure to support high-volume production workloads with low latency. • Built scalable data processing pipelines using Python, SQL, and Spark, enabling efficient handling of large-scale structured and unstructured datasets. • Developed intelligent recommendation and personalization systems using machine learning algorithms to enhance user engagement and improve business conversion rates. • Applied advanced ensemble learning techniques and hybrid modeling approaches to improve predic

Data Scientist

Infosys

Jan 2017 - Apr 2019 (2 years 4 months)

• Built and deployed end-to-end data-driven analytical systems, integrating data pipelines, statistical analysis, and machine learning models to deliver scalable business insights and automation. • Developed automated data processing and modeling workflows, reducing manual effort and improving efficiency in data preparation, feature engineering, and model training. • Implemented advanced statistical modeling techniques, including regression analysis, probability modeling, and multivariate analysis to extract deep insights from complex datasets. • Engineered robust data cleaning and preprocessing frameworks, handling missing values, outliers, and inconsistent data to ensure high-quality analytical outputs. • Built customer segmentation and b

Chandu Reddy

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