PavanYellathakota
Hey DataGeeks, I'm Pavan 👋
I am a Data Explorer passionate about diving into every field where data is prominent. My journey spans the full spectrum from Market Research & Supply Chain Analytics to designing Databases & ETL Pipelines. I extend this expertise into AI, developing Machine Learning and Deep Learning models with a specific focus on BERT-based Text & Semantic Analysis.
Data Scientist | ML Engineer | Product Analytics
📍 Location : Seattle, WA, USA
📞 Mobile : +1 (929) 278-4589
✉️ Email : pavan.yellathakota.ds@gmail.com
Linkedin : https://linkedin.com/in/yellatp
GitHub : https://github.com/yellatp
👨💻 Professional Summary
Data Scientist with 3+ years of experience developing predictive models and automated data infrastructure. Proven track record in improving search precision, designing quantitative research pipelines, and implementing data-driven solutions for marketing and product growth. Skilled in bridging the gap between data engineering and stakeholder decision-making through statistical validation, A/B testing, and interactive analytics.
� Core Competencies
* **Statistical Analysis**: Exploratory Data Analysis (EDA), Hypothesis Testing, A/B Testing, Confidence Intervals, Statistical Inference.
* **Machine Learning**: Regression & Classification, Time-Series Forecasting, Feature Engineering, Natural Language Processing (NLP), Clustering, Model Evaluation.
* **Product Analytics**: User Segmentation, Funnel Analysis, Cohort Tracking, KPI Definition, Data Visualization & Storytelling.
🛠️ Technical Skills
| Domain | Stack |
|---|---|
| Languages & Databases |         |
| AWS Cloud Data |        |
| ML Frameworks |      |
| Tools & Visualization |      |
💼 Professional Experience
Alphonso AI, Shipley Center for Innovation | Junior Data Scientist
Potsdam, NY | Jul 2025 – Present
- Search & Retrieval: Engineered a hybrid candidate retrieval system combining BM25 keyword search with Vector Embeddings; fine-tuned embedding models to domain-specific data, improving search precision by 38% for top-10 results.
- Reranking: Deployed a Cross-Encoder reranking step to filter hallucinations from LLM outputs, ensuring 95%+ relevance in final recommendations.
- Causal Inference: Developed engagement scoring models using CausalML to distinguish true user intent from noise, identifying critical drop-off points in the candidate funnel that guided UI/UX redesigns.
- ML Infrastructure: Built an end-to-end data pipeline on AWS (S3, Glue) to automate feature extraction from behavioral logs and implemented drift monitoring via Grafana to ensure stability.
Student Managed Investment Fund, Clarkson University | Graduate Quantitative Researcher
Potsdam, NY | Sep 2024 – Apr 2025
- Portfolio Management: Managed a $650K real-capital portfolio, delivering a 51% total return and outperforming the S&P 500 benchmark by 26% (2,600 bps).
- Alternative Data Pipeline: Built a sentiment analysis engine scraping Reddit/YouTube to validate fundamental buy signals, using BERT-based sentiment scoring to overlay quantitative signals on traditional financial metrics.
- Automation: Automated the extraction of financial statements from SEC EDGAR using Python & Vertex AI, reducing data collection time by 80% for the analyst team.
- Risk Modeling: Developed Monte Carlo simulations and risk-parity models to stress-test overweight positions and quantify potential drawdowns for high-conviction trades.
HAVK Mladost (Elite Athletics Club) | Graduate Data Science Consultant
Potsdam, NY | Oct 2023 – May 2025
- Cloud Migration: Architected a centralized data lake on AWS S3, migrating legacy records to a queryable cloud environment and reducing data retrieval latency by 40%.
- ETL Optimization: Developed PySpark ETL jobs on AWS Glue to process 1M+ cross-channel events; utilized partition pruning to optimize query costs and speed.
- Uplift Modeling: Applied uplift modeling and behavioral clustering to identify high-value fan segments, optimizing marketing spend and merchandise revenue.
- Performance Analytics: Developed backend services with FastAPI and built interactive dashboards that delivered real-time performance insights to World Championship coaches.
eAppSys Limited | Business Data Analyst
Hyderabad, India | Jul 2022 – Dec 2022
- Forecasting: Developed demand forecasting models (Prophet/SARIMAX) for 1,500+ SKUs, integrating exogenous variables (holidays, promotions) to improve forecast accuracy (MAPE) by 15%.
- Reporting Automation: Designed and deployed automated KPI dashboards in Oracle Analytics Cloud (OAC), saving the procurement team 12+ hours/week of manual reporting time.
- ML Workflows: Implemented GxP-compliant ML workflows on Oracle Cloud Infrastructure (OCI) with real-time alerts, achieving 99.9% uptime for critical inventory monitoring.
Kantar GDC India | Data Analyst
Pune, India | Sep 2021 – May 2022
- Pipeline Automation: Built automated data pipelines for Tracker and Syndicated Research projects using Python and PySpark, integrating 10M+ survey records from 30+ sources and reducing processing latency by 30%.
- Statistical Analysis: Developed sampling approaches and statistical significance testing to ensure data representativeness across Middle East and Central Africa markets.
- Consumer Insights: Supported recurring monthly/quarterly client tracking projects by developing regression models and delivering insights for 10+ FMCG and Telecom clients.
🏗️ Some Notable Projects
| Project | Description | Tech Stack |
|:---:|:---|:---:|
| **[Text-Analysis-using-NLP-LDA](https://github.com/yellatp/Text-Analysis-using-NLP-LDA)** | NLP project focused on topic modeling and text analysis. | NLP, LDA, Python |
| **[Detoxify Telugu](https://github.com/yellatp/detoxify-telugu)** | Toxic comment classification for Telugu language. | NLP, Deep Learning |
| **[Synthetic Data Generator](https://github.com/yellatp/Synthetic-Data-Generator)** | Tool to generate synthetic datasets for testing/training. | Python, Data Gen |
| **[BingeMax Recommendation Engine](https://github.com/yellatp/BingeMax-Personalized-Movie-Recommendation-Engine)** | Personalized movie recommendation system. | ML, Recommender Systems |
| **[Fintech Sales GAP Analysis](https://github.com/yellatp/Fintech-Sales-GAP-Analysis)** | Analyzing sales gaps in fintech products. | Data Analysis, Visualization |
| **[KonnectR Fullstack App](https://github.com/yellatp/KonnectR_flask_fullstack_app)** | Fullstack web application built with Flask. | Flask, Python, Web |
| **[PreOwned Cars Price Prediction](https://github.com/yellatp/PreOwnedCars_Price_Prediction_Model_V1.0)** | ML model to predict prices of used cars. | Regression, Scikit-learn |
| **[Fake News Classifier](https://github.com/yellatp/Fake-News-Classifier)** | Identification of fake news articles using ML. | Classification, NLP |
| **[Content Strategy Netflix](https://github.com/yellatp/Content-Strategy-Analysis-NETFLIX)** | Data-driven strategy analysis for Netflix content. | Data Science, EDA |
| **[Supply Chain Analysis](https://github.com/yellatp/Supply-Chain-Analysis-Python)** | Optimization and analysis of supply chain data. | Python, Logistics |
| **[GenZ Career Preferences](https://github.com/yellatp/GenZ-Career-Preferences-Report)** | Analysis report on GenZ career trends. | Research, Analytics |
| **[Website A/B Testing](https://github.com/yellatp/Website-AB-Testing-Python)** | Statistical analysis of A/B test results. | Statistics, Python |
Last Updated: 2026 by PAVAN YELLATHAKOTA </sub> </p>