Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

Hi there! I’m Rakshala Mukka, a senior data scientist blending advanced ML, AI, and data engineering to solve complex challenges in finance and healthcare. I thrive on building end-to-end AI lifecycles, from data prep and model training to deployment and governance, with a focus on scalable, compliant solutions that deliver real business impact. I enjoy bridging the gap between cutting-edge AI innovation and practical product goals, collaborating with stakeholders to translate needs into reliable data platforms, robust ML pipelines, and user-friendly analytics that empower teams to act quickly and confidently.…Hi there! I’m Rakshala Mukka, a senior data scientist blending advanced ML, AI, and data engineering to solve complex challenges in finance and healthcare. I thrive on building end-to-end AI lifecycles, from data prep and model training to deployment and governance, with a focus on scalable, compliant solutions that deliver real business impact. I enjoy bridging the gap between cutting-edge AI innovation and practical product goals, collaborating with stakeholders to translate needs into reliable data platforms, robust ML pipelines, and user-friendly analytics that empower teams to act quickly and confidently.

Rakshala Mukka

Data Scientist, AI Engineer, Full Stack Developer, +1





Hi there! I’m Rakshala Mukka, a senior data scientist blending advanced ML, AI, and data engineering to solve complex challenges in finance and healthcare. I thrive on building end-to-end AI lifecycles, from data prep and model training to deployment and governance, with a focus on scalable, compliant solutions that deliver real business impact. I enjoy bridging the gap between cutting-edge AI innovation and practical product goals, collaborating with stakeholders to translate needs into reliable data platforms, robust ML pipelines, and user-friendly analytics that empower teams to act quickly and confidently.…Hi there! I’m Rakshala Mukka, a senior data scientist blending advanced ML, AI, and data engineering to solve complex challenges in finance and healthcare. I thrive on building end-to-end AI lifecycles, from data prep and model training to deployment and governance, with a focus on scalable, compliant solutions that deliver real business impact. I enjoy bridging the gap between cutting-edge AI innovation and practical product goals, collaborating with stakeholders to translate needs into reliable data platforms, robust ML pipelines, and user-friendly analytics that empower teams to act quickly and confidently.

Available to hire

Hi there! I’m Rakshala Mukka, a senior data scientist blending advanced ML, AI, and data engineering to solve complex challenges in finance and healthcare. I thrive on building end-to-end AI lifecycles, from data prep and model training to deployment and governance, with a focus on scalable, compliant solutions that deliver real business impact.

I enjoy bridging the gap between cutting-edge AI innovation and practical product goals, collaborating with stakeholders to translate needs into reliable data platforms, robust ML pipelines, and user-friendly analytics that empower teams to act quickly and confidently.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Senior Data Scientist at Bank of America

August 1, 2024 - Present

Partnered with stakeholders across DCRS to define KPIs and translate complex banking documentation workflows into end-to-end ML, Gen AI, and data-engineering solutions, driving measurable improvements in revenue, latency, and operational efficiency. Designed and managed an ADLS Gen2 storage layer for document signatures, artifacts, and client artifacts, integrated with Azure Data Factory ETL pipelines to automate ingestion, transformation, and routing of document data across upstream and downstream banking systems. Built real-time and batch data pipelines in Azure Event Hubs, Azure Stream Analytics, PySpark, and SQL, processing 50M+ transactions per day with sub-200ms latency, ensuring clean, analytics-ready data for downstream consumers. Engineered a distributed RAG system using LangChain, Azure OpenAI, Azure AI Search, and FAISS with semantic ranking over vector stores, achieving 95%+ accuracy on financial document queries and policy lookups. Exposed scalable Python-based REST APIs (

Senior Data Scientist at IT America Inc

June 1, 2023 - July 1, 2024

Partnered with business stakeholders and SMEs to define KPIs, success metrics, and translate complex revenue-cycle management (RCM) financial problems into actionable data science and ML solutions. Engineered and maintained scalable ETL/ELT pipelines using Python, SQL, and SSIS, ensuring high data quality, consistency, and readiness across multiple healthcare platforms. Designed domain-specific feature engineering pipelines around accounting, denial codes, transaction patterns, and payer behavior to optimize model performance. Migrated legacy ETL tasks to Databricks with PySpark and Delta Lake to improve data reliability, ACID compliance, and versioning. Delivered structured analytics and reporting layers to support revenue forecasting, denial prioritization, and account reconciliation. Built end-to-end ML production pipelines using Python (XGBoost, scikit-learn) with end-to-end MLOps workflows and model versioning for consistent production quality. Implemented HIPAA-compliant data han

Data Scientist at Accenture

August 1, 2021 - September 1, 2022

Analyzed customer policy and claims data to model behavior, policy retention, and risk assessment. Built scalable ETL pipelines using AWS services (Amazon RDS, Amazon Redshift, AWS Glue) to ingest, transform, and load large-scale insurance data for analytics and modeling. Designed domain-specific feature engineering pipelines covering underwriting, denial codes, transaction patterns, and payer behavior to optimize model performance. Applied NLP techniques (tokenization, sentiment analysis) using Python (NLTK, spaCy) and Amazon SageMaker. Built and deployed ML models via Amazon SageMaker with automated pipelines (SageMaker Pipelines) for reproducible training, deployment, and monitoring. Created interactive dashboards in Amazon QuickSight to present insights to stakeholders and enable data-driven decision making. Ensured secure data handling in line with healthcare data regulations and internal governance. Orchestrated end-to-end ML lifecycles with versioning and CI/CD practices to acce

Machine Learning Engineer at DBS Bank

August 1, 2020 - July 1, 2021

Identified digital banking challenges and built ML solutions for churn prediction, segmentation, and fraud detection. Architected distributed, fault-tolerant ETL pipelines using S3, AWS Glue, Redshift, and Spark (Glue/EMR). Applied NLP techniques (tokenization, sentiment analysis) using Python (NLTK, spaCy) and AWS Comprehend. Built and deployed ML models using AWS SageMaker with automated pipelines to streamline model training, evaluation, and deployment. Developed and delivered robust analytics and reporting layers to support business decisions and risk management. Implemented scalable data-processing workflows to enable timely insights across banking lines of business.

Data Scientist at IT America Inc

June 1, 2023 - July 1, 2024

Partnered with business stakeholders and SMEs to define KPIs and success metrics, translating complex RCM financial problems into actionable data science and ML solutions. Engineered scalable ETL/ELT pipelines using Python, SQL, and SSIS to ingest, transform, and load large healthcare data across multiple health plan systems, ensuring high data quality and consistency. Designed domain-specific feature engineering pipelines including accounting, denial codes, transaction patterns, and payer behavior to optimize model performance. Migrated legacy ETL tasks to Databricks, leveraging PySpark and Delta Lake to ensure data reliability and ACID compliance for patient financial records. Delivered analytics and reporting layers to support revenue forecasting, denial prioritization, and account reconciliation; built interactive dashboards to provide executive insights.