SANIKA NARAYANPETHKAR
Machine Learning Engineer
Arlington,VA
Summary
Experienced machine learning developer with expertise in designing, building, and maintaining end-to-end ML systems for high-performance, large-scale knowledge extraction. Proficient in integrating research and production environments, utilizing automated pipelines, predictive modeling, and cloud infrastructure. Demonstrated success in improving model efficiency and scaling solutions. Recognized for collaborative mindset and strong problem-solving abilities, with a solid foundation in software engineering and applied machine learning. Particularly skilled in research-driven and experimental ML settings.
Overview
3
3
years of professional experience
Work History
ML/ Data Science Capstone Fellows Program
World Bank
Washington Dc
01.2025 - Current
- Developed end-to-end machine learning systems for geospatial risk assessment, analyzing land systems and mapping over 5,000 sites, which improved risk detection accuracy by 35% and reduced environmental vulnerabilities by 30%
- Built end-to-end ETL and modeling pipelines using geospatial and satellite imagery data, reducing processing time by 50% and boosting prediction efficiency by 40%, while supporting sustainability policy adoption across 50+ projects
- Collaborated with researchers to design high-performance, end-to-end ML pipelines that integrated multi-source data, streamlined experimentation workflows, and accelerated model validation cycles for geospatial analysis
AI/ML Intern
AICTE
Hyderabad, India
03.2022 - 05.2022
- Developed and deployed machine learning models on AWS SageMaker, improving prediction accuracy by 20 percent through advanced hyperparameter tuning and feature engineering, leading to a 15 percent increase in inference efficiency
- Optimized over 1TB of structured and unstructured data on AWS S3, reducing data retrieval time by 15 percent and enhancing model training performance through efficient data pipeline design
- Designed scalable ML workflows using AWS Lambda and Step Functions, automating model deployment and monitoring, reducing operational overhead by 30 percent
- Collaborated with cross-functional teams to define modeling objectives and ensure robust experimental design for cloud-based ML deployments
Education
Master of Science - Data Science
George Washington University
DC 05.2001 -
Bachelor of Science - Computer Science
Sreenidhi Institute of Science And Technology
India 08.2019 - 08.2023
Skills
- Python
- SQL
- Machine Learning Frameworks
- NLP
- Deep Learning
- MLops
- Version Control
- Cloud Computing
- Data Engineering
- Project Management & Problem-Solving
- Power BI
- Tableau
Timeline
ML/ Data Science Capstone Fellows Program
World Bank
01.2025 - Current
AI/ML Intern
AICTE
03.2022 - 05.2022
Bachelor of Science - Computer Science
Sreenidhi Institute of Science And Technology
08.2019 - 08.2023
Master of Science - Data Science
George Washington University
05.2001 -
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