Machine Learning, Data Scientist,Prescriptive and Predictive Analytics

Hari Priya Ramamoorthy

***********.*@************.*** 857-***-**** Boston, MA GitHub LinkedIn Coursera EDUCATION

Northeastern University, Boston, MA GPA: 4.00

Master of Science in Analytics, Concentration in Applied Machine Intelligence Jul 2024 - Aug 2025 Recipient of CPS Leaders and Scholars Scholarship Awards for competitive academic and personal leadership achievements. SASTRA University, Thanjavur, India GPA: 3.70

Bachelor of Technology in Computer Science Jun 2015 - May 2019 TECHNICAL SKILLS

Programming : Python (NumPy, Pandas, Scikit-Learn, OpenCV, PyTorch), R, SQL Machine Learning : Bayesian statistics, A/B hypothesis test, Decision Tree, Regression, ML optimization, Clustering, NLP Topic Model Databases : BigQuery, Oracle, PostgreSQL, SQL Server, MongoDB, Snowflake GenAI : Prompt Engineering, LLM Fine-Tuning, RAG, FAISS, LangGraph, Gemini, AI Agents ETL Automation : Airflow, Cloud Build, MLFlow, Tekton, Terraform, REST API, FastAPI, Kafka Cloud Platforms : GCP (BigQuery, Pub/Sub, Vertex AI, Cloud Storage, Kubernetes), AWS, Databricks, Data Integration : Alteryx, Talend, Cloud Run, Cloud Functions, DataFlow BI Tools : Tableau, Qlik, Power BI, Looker

WORK EXPERIENCE

Graduate Research Assistant at Supply Trace, Northeastern University - Boston, MA Sep 2024 - Present

• Built a scalable data lake architecture on GCP with Airflow ETL automation pipelines for automating large scale ingest-transform- load workflows with object-oriented testing, reducing manual effort by 40%.

• Developed Chinese address de-duplication algorithm using Llama-3 LLM and machine learning clustering techniques, achieving 80% accuracy in address matching, which enhanced data integrity for research project.

• Engineered serverless Cloud Functions leveraging ArcGIS API for automated geocoding and achieved 95% geolocation accuracy. Senior Data Scientist at Ford Motor Company - Chennai, India Apr 2022 - Mar 2024

• Collaborated with agile cross-functional teams to implement end-to-end GenAI/ML and BI solutions for supply chain and Finance Teams, identifying in-potential $2M+ annual cost savings.

• Delivered effective presentations to stakeholders, aligning technical solutions with business priorities and requirements.

• Accomplished a 60% reduction in supplier overpayments by automating audit document summarization, by fine-tuning the LLM using the GenAI RAG LangChain and prompt engineering model.

• Developed and deployed predictive risk classification models in Python on GCP Vertex AI, enhancing supplier management by 40%.

• Forecasted raw material prices using time series ARIMA and regression models, improving procurement budget planning by 70%.

• Migrated and optimized ETL workflows from Alteryx to Python by 45% using airflow, cloud build, cloud scheduler CI/CD pipelines.

• Recognized as “Best Agile Mind” for pioneering the automation of ML and BI pipelines on GCP, reducing release cycle times. Data Scientist at Ford Motor Company - Chennai, India Jul 2019 - Mar 2022

• Maintained ML risk models and Qlik dashboards to optimize supply chain KPIs, improving visibility and reporting delays by 30%.

• Built an LDA-based NLP recommendation model with AngularJS front-end, boosting cost-save suggestions by 20% for engineers.

• Performed ad-hoc statistical analysis and EDA using advanced Python, R, SQL and Alteryx to uncover actionable business insights.

• Developed a KPI metrics dashboard and data models integrating cloud, Hadoop, and SQL databases, to streamline audit process.

• Authored deployment guides of ML workflows enhancing knowledge transfer and reducing new joiner's training period by 30%. ACADEMIC PROJECTS

MBTA On-Time Prediction Analysis GitHub Oct 2024 - Dec 2024

• Improved MBTA on-time prediction by identifying key delay factors via ANOVA, Chi-square, and A/B testing; built optimized regression models using stepwise and Lasso in R for better interpretability.

• Delivered data-driven recommendations to enhance transit reliability and rider experience using predictive modeling. Experiential Learning Program at Power of Patients LLC, Northeastern University - Boston, MA Jan 2025 - Mar 2025

• Conducted advanced data wrangling, hypothesis tests and data mining on patient healthcare records to uncover correlations between Traumatic Brain Injury (TBI) incidence and weather patterns, visualized insights via Tableau for effective data storytelling.

• Applied clustering and PCA techniques to segment patients by TBI patterns, enhancing personalized recommendations by 25%. World Bank Mortality Analysis Tableau Jan 2025 – Mar 2025

• Delivered data-driven recommendations for WHO by visualizing factors affecting mortality and life expectancy with Tableau.

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