Data Scientist, ML or AI Engineer
- Location
- United States
- Rate, USD
- $30 / hour
- Work schedule
- Full Time,
- Language skills
- English
- Available for Hire
- Yes
About me
I am a Data Scientist with a strong foundation in NLP, machine learning, and large language models (LLMs), with applied experience across real-world projects in public policy and finance. I have led topic modeling workflows at The World Bank using LLaMA 2, BERTopic, Mistral, and LangChain to extract structured insights from over 26,000 unstructured conflict records. My expertise includes designing scalable ML pipelines leveraging AWS EC2, S3, and Lambda for cloud-based experimentation and automation. I have a proven ability to deliver reproducible, production-ready code and actionable insights in fast-paced, high-stakes environments. I am passionate about using GenAI and cloud infrastructure to solve complex challenges in data-rich domains.
Professional area
Education
- Aug 2023 - May 2025 Master of Science in Data Science @ The George Washington University
- Aug 2019 - May 2023 Bachelor of Engineering in Computer Engineering @ Presidency University
Experience
- Jan 2025 – May 2025 Capstone Fellow @ The World Bank
Built robust, reproducible pipelines to clean, enrich, and transform over 26,000 unstructured conflict event records from the ACLED dataset, enabling high-quality GenAI analysis. Applied advanced topic modeling techniques using LLaMA 2, BERTopic, and LangChain to extract latent protest themes, boosting topic coherence by 27% and topic diversity by 120%. Utilized AWS EC2 instances to run compute-intensive LLM workflows, and AWS S3 for efficient data management and sharing. Automated parts of the workflow using AWS Lambda to reduce manual overhead and streamline preprocessing. Delivered structured insights via Jupyter Notebooks and technical reports, improving stakeholder onboarding and decision support.
- Sep 2024 – Dec 2024 Data Science Intern @ Springer Capital
Cleaned and analyzed over 1 million financial and operational records from multiple data sources, ensuring 99% data accuracy for downstream analytics. Automated recurring tasks using Microsoft Power Automate, reducing manual workload by 30% and saving over 15 hours per week. Developed interactive Power BI dashboards and performed exploratory and statistical analysis using SQL and Python, enabling data-driven decisions and improving process efficiency by 20%.
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