I am a Data Scientist and AI/ML Engineer with a Master's degree in Data Science from the University of Wisconsin–Milwaukee, graduating in May 2025. With around 4 years of experience in delivering AI/ML and data-driven solutions across financial services, IT consulting, and enterprise platforms, I specialize in building scalable ML/AI pipelines, predictive modeling, NLP, and Generative AI including large language models and retrieval-augmented generation systems. I take pride in creating data solutions that drive measurable business outcomes such as efficiency gains and enhanced decision-making. In my current role at State Street, I engineer secure cloud-based data pipelines and design models for fraud detection and market risk forecasting, integrating generative AI chatbots to boost client engagement. Previously, at HCL Tech, I built scalable machine learning pipelines and deep learning models, led Agile AI initiatives, and mentored teams in MLOps best practices. I am passionate about Agile methodologies, compliance-driven analytics, and using cloud-native technologies to build scalable and audit-ready AI systems.

Apurva Rajendra Shinde

I am a Data Scientist and AI/ML Engineer with a Master's degree in Data Science from the University of Wisconsin–Milwaukee, graduating in May 2025. With around 4 years of experience in delivering AI/ML and data-driven solutions across financial services, IT consulting, and enterprise platforms, I specialize in building scalable ML/AI pipelines, predictive modeling, NLP, and Generative AI including large language models and retrieval-augmented generation systems. I take pride in creating data solutions that drive measurable business outcomes such as efficiency gains and enhanced decision-making. In my current role at State Street, I engineer secure cloud-based data pipelines and design models for fraud detection and market risk forecasting, integrating generative AI chatbots to boost client engagement. Previously, at HCL Tech, I built scalable machine learning pipelines and deep learning models, led Agile AI initiatives, and mentored teams in MLOps best practices. I am passionate about Agile methodologies, compliance-driven analytics, and using cloud-native technologies to build scalable and audit-ready AI systems.

Available to hire

I am a Data Scientist and AI/ML Engineer with a Master’s degree in Data Science from the University of Wisconsin–Milwaukee, graduating in May 2025. With around 4 years of experience in delivering AI/ML and data-driven solutions across financial services, IT consulting, and enterprise platforms, I specialize in building scalable ML/AI pipelines, predictive modeling, NLP, and Generative AI including large language models and retrieval-augmented generation systems. I take pride in creating data solutions that drive measurable business outcomes such as efficiency gains and enhanced decision-making.

In my current role at State Street, I engineer secure cloud-based data pipelines and design models for fraud detection and market risk forecasting, integrating generative AI chatbots to boost client engagement. Previously, at HCL Tech, I built scalable machine learning pipelines and deep learning models, led Agile AI initiatives, and mentored teams in MLOps best practices. I am passionate about Agile methodologies, compliance-driven analytics, and using cloud-native technologies to build scalable and audit-ready AI systems.

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Work Experience

Data Scientist at State Street
January 1, 2025 - Present
Engineered secure AWS-based data pipelines using Glue, Redshift, and S3 for real-time trade analytics, transaction monitoring, and financial reporting, improving query latency and reporting efficiency by 30–40%. Designed and optimized PostgreSQL and Oracle databases with compliance-focused architecture for trade settlements and asset management workflows. Automated model deployment pipelines using CodePipeline, Docker, and GCP Kubernetes, ensuring scalable and audit-ready analytics workflows aligned with enterprise standards. Developed and deployed ML models such as Logistic Regression, Gradient Boosting, and CNNs for fraud detection and credit risk prediction, reducing data processing time by 25%. Built AI/ML solutions leveraging SageMaker, Bedrock, and LangChain for market risk forecasting, client behavior prediction, and NLP-driven financial document summarization. Integrated structured and unstructured data with RAG models and deployed GenAI chatbots for client servicing and pers
AI/ML Engineer at HCL Tech
August 1, 2023 - September 5, 2025
Built scalable ML pipelines on AWS using SageMaker, EC2, Lambda, Redshift, and S3 for large-scale data ingestion, demand forecasting, and transaction analytics, achieving 25% improvement in forecasting accuracy. Applied TensorFlow, PyTorch, and Keras for predictive analytics, fraud detection, and computer vision tasks like document and ID verification. Developed XGBoost-based risk scoring engines and NLP pipelines utilizing spaCy and BERT on unstructured e-commerce data to automate workflows, improving operational efficiency by 20%. Led Agile AI/ML projects, collaborating with consulting and software teams to integrate ML into enterprise platforms, reducing data processing latency by 15% and accelerating release velocity globally. Designed deep learning models including CNNs, RNNs, LSTMs, and Transformers for anomaly detection and real-time personalization with over 90% accuracy. Mentored junior engineers on MLOps best practices including CI/CD and Infrastructure-as-Code deployments. F

Education

Master of Science in Data Science at University of Wisconsin–Milwaukee
January 11, 2030 - May 1, 2025

Qualifications

Snowpro Associate: Platform certification
January 11, 2030 - September 5, 2025
Machine learning Pipelines with Azure ML Studio (Coursera)
January 11, 2030 - September 5, 2025

Industry Experience

Financial Services