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I am a data scientist with 6+ years of experience designing and deploying scalable ML solutions across NLP, computer vision, and Generative AI. I enjoy turning data into decisions by combining statistical methods with deep learning, delivering impact in fraud detection, customer insights, and forecasting. I write clean Python, build end-to-end ML pipelines, and deploy models in the cloud with AWS, GCP, and Azure. I collaborate with cross-functional teams and strive for maintainable, auditable solutions using MLOps practices like Docker, MLflow, Git, CI/CD, and Terraform.…I am a data scientist with 6+ years of experience designing and deploying scalable ML solutions across NLP, computer vision, and Generative AI. I enjoy turning data into decisions by combining statistical methods with deep learning, delivering impact in fraud detection, customer insights, and forecasting. I write clean Python, build end-to-end ML pipelines, and deploy models in the cloud with AWS, GCP, and Azure. I collaborate with cross-functional teams and strive for maintainable, auditable solutions using MLOps practices like Docker, MLflow, Git, CI/CD, and Terraform.

Sreedhar Reddy Illuru

Data Scientist, AI Engineer, AI Developer, +2

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I am a data scientist with 6+ years of experience designing and deploying scalable ML solutions across NLP, computer vision, and Generative AI. I enjoy turning data into decisions by combining statistical methods with deep learning, delivering impact in fraud detection, customer insights, and forecasting. I write clean Python, build end-to-end ML pipelines, and deploy models in the cloud with AWS, GCP, and Azure. I collaborate with cross-functional teams and strive for maintainable, auditable solutions using MLOps practices like Docker, MLflow, Git, CI/CD, and Terraform.…I am a data scientist with 6+ years of experience designing and deploying scalable ML solutions across NLP, computer vision, and Generative AI. I enjoy turning data into decisions by combining statistical methods with deep learning, delivering impact in fraud detection, customer insights, and forecasting. I write clean Python, build end-to-end ML pipelines, and deploy models in the cloud with AWS, GCP, and Azure. I collaborate with cross-functional teams and strive for maintainable, auditable solutions using MLOps practices like Docker, MLflow, Git, CI/CD, and Terraform.

Available to hire

I am a data scientist with 6+ years of experience designing and deploying scalable ML solutions across NLP, computer vision, and Generative AI. I enjoy turning data into decisions by combining statistical methods with deep learning, delivering impact in fraud detection, customer insights, and forecasting. I write clean Python, build end-to-end ML pipelines, and deploy models in the cloud with AWS, GCP, and Azure. I collaborate with cross-functional teams and strive for maintainable, auditable solutions using MLOps practices like Docker, MLflow, Git, CI/CD, and Terraform.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Data Scientist at Allstate

October 1, 2024 - Present

Designed a high-performance XGBoost-based scoring model to detect transactional anomalies, increasing fraud capture by 1,500 cases per month and reducing false positives by 300+ cases monthly, while aligning with enterprise compliance goals. Engineered deep behavioral features from 12M+ records using SQL and Python, revealing customer-level patterns that identified 5,000 additional suspicious accounts annually. Built real-time Tableau dashboards for fraud trends and operational metrics, saving 35+ hours of monthly reporting and enabling faster leadership decisions. Applied BERT-based NLP to complaint and case summaries to surface early fraud signals and streamline escalation. Automated multi-step data extraction and validation workflows via Python and internal APIs to improve data availability for downstream analytics.

Data Scientist at Value Labs

July 1, 2023 - October 16, 2025

Developed a CNN-LSTM model in TensorFlow to forecast sales trends, reducing stockouts and ensuring timely replenishment of 1,200+ products monthly, boosting shelf availability and client revenue. Built a random forest churn predictor, improving recall and retaining 500+ high-value accounts in the first deployment. Cleaned and aggregated behavior logs using SQL, removing millions of redundant records and improving data quality. Deployed models on AWS EC2 with CloudWatch monitoring, maintaining latency under 200ms, and visualized segment KPIs in Tableau to enhance marketing ROI.

Data Scientist at Accenture

July 1, 2022 - October 16, 2025

Led an NLP ticket classification system using TF-IDF, automating routing for 1,000+ tickets per day and improving support throughput. Architected features on log data with Pandas to boost model precision and reduce quarterly downtime by 200+ events. Created an Isolation Forest-based anomaly detection pipeline, identifying 19 critical billing errors within the first month and saving over $130K in quarterly corrections. Built LSTM-based time-series forecasting for telecom capacity planning, enabling expansion to 10 new service regions. Collaborated with stakeholders to translate data insights into actionable strategies and streamlined weekly dashboards with Matplotlib and Seaborn; conducted rigorous A/B testing with scikit-learn.