🎓 MS Data Science @ ASU
💡 ML Engineering • Data Science • Distributed Analytics • Ranking Systems • Data Analytics
📍 Tempe, AZ • Seeking Summer 2026 ML/Data Science Internships

Graduate student at ASU specializing in production-style machine learning, with hands-on experience building:

• Learning-to-Rank (LTR) pipelines
• Engagement prediction models
• Fairness & explainability workflows
• Deep learning forecasting systems
• Distributed analytics using Apache Spark

I enjoy turning raw data into reliable, scalable ML systems that deliver measurable impact.

Production-style LTR pipeline with offline training + online scoring.
Tech: LightGBM (LambdaMART), FastAPI, PyTest
Focus: NDCG, MAP, feature generation, registry

End-to-end ML pipeline with fairness auditing.
Tech: Scikit-Learn, Fairlearn
Focus: DPD/EOD metrics, leakage prevention, reproducibility

Model interpretation using SHAP, LIME, permutation importance.

Fairness assessment across sensitive groups with group-wise metrics and ethical evaluation.

Large-scale geospatial processing with PySpark.

Beginner-friendly archive showing my early journey in EDA, ML basics, NLP, and deep learning.

Machine Learning: Scikit-learn, LightGBM, XGBoost, LSTM, ARIMA, Prophet
Ranking & Recsys: LTR, LambdaMART, feature pipelines, evaluation
Fairness & Explainability: SHAP, LIME, Fairlearn, AIF360
Data Engineering: Apache Spark, PySpark, ETL pipelines
Databases: PostgreSQL, SQL, NoSQL (RocksDB)
Tools: FastAPI, Git, PyTest, Linux, MLflow (exposure)
Visualization: Tableau, Matplotlib, Seaborn

Arizona State University
M.S. Data Science (Decision & Computing Analytics)

📧 psjharshni@gmail.com
🔗 LinkedIn: https://www.linkedin.com/in/jananya-harshni-74718b261/
🔗 GitHub: github.com/JananyaPS

✨ This profile reflects my journey from first principles → production-ready ML systems. Always open to collaborations, internships, and research opportunities.