M Prathyun Kumar Reddy
Data Scientist • p*****************@gmail.com • +91*******014 • drivetube.ai/•••••
Professional Summary
Data Scientist with 0 years of experience building end-to-end ML systems, research publications, and business reporting solutions. Experienced with Python-based data pipelines using Pandas and SQL to prepare features for supervised models and explainability workflows using SHAP. Built and deployed production-style model APIs and interactive frontends with FastAPI and Streamlit and hosted artifacts on AWS (S3, EC2). Developed interactive Power BI dashboards and automated reporting pipelines to translate model outputs into stakeholder-ready KPIs. Led an IEEE-published research project applying Random Forest and explainable ML to startup survival prediction. Comfortable with model training, evaluation, hyperparameter tuning, and cross-validation using Scikit-learn, and with version control and reproducible notebooks (Git, Jupyter). Seeking an entry-level Data Science or ML Engineering role to deliver measurable business impact.
Technical Skills
Programming Language: Python,SQL
Backend Technologies: FastAPI
Databases: MongoDB
Cloud Platforms: Amazon Web Services,EC2,S3
Version Control & Development Tools: Git,GitHub
Data Analysis & Visualization: Pandas,Streamlit,Plotly,Seaborn,Power BI
Machine Learning & AI: Random Forest,Logistic Regression,SHAP
AI/ML Frameworks & Libraries: scikit-learn,XGBoost
Work Experience
Rubixe™
Data Scientist Intern
Mar 2026 – Present
Worked at Rubixe Technologies, an AI and automation solutions company; supported client analytics and ML initiatives by building data pipelines, dashboards, and prototype models.
Tech Stack: Python, Pandas, SQL, Power BI, Scikit-learn, Git
- Collected, cleaned, and transformed structured datasets by writing SQL queries and constructing Pandas pipelines to prepare feature tables for analytics and modelling.
- Conducted exploratory data analysis with Pandas and Seaborn to surface trends and anomalies that informed KPI definitions and downstream dashboards.
- Designed interactive Power BI dashboards with DAX measures and dynamic filters to present KPIs and model outputs to technical and non-technical stakeholders.
- Implemented classification and regression experiments in Scikit-learn, applying feature engineering, cross-validation, and hyperparameter search to validate model choices.
- Automated recurring reporting and feature-preparation workflows using Python scripts and Jupyter Notebooks to accelerate delivery of insights to the business.
- Documented analysis methodologies, maintained version-controlled code in Git, and produced reproducible notebooks to enable team knowledge transfer.
Projects
India Startup Survival Predictor | 2025 – 2026
Tools Used: Pandas, SQL, Random Forest, XGBoost, SHAP, FastAPI, Streamlit, AWS, S3, EC2, Plotly, GitHub
- Led a 3-person team to research and build an explainable ML pipeline predicting startup survival; published the research at IEEE ICETEMS 2026 (Scopus indexed).
- Engineered 30+ features from financial and ecosystem datasets using SQL and Pandas to construct model-ready datasets for supervised learning.
- Trained and tuned Random Forest and XGBoost models using cross-validation and hyperparameter search, achieving 98% accuracy and 99.79% ROC-AUC on the evaluation split.
- Applied SHAP to produce feature-level explainability and business-facing narratives that identified key risk and success factors for startups.
- Developed FastAPI endpoints and integrated a Streamlit frontend for real-time predictions and analysis; deployed model artifacts to AWS S3 and served the application on EC2.
AI Resume Screening & Candidate Ranking System | 2024 – 2025
Tools Used: Python, MongoDB, TF-IDF, Logistic Regression, Random Forest, XGBoost, Streamlit
- Built an ATS prototype that parsed PDF/DOCX resumes with Python, extracted structured attributes, and stored parsed records in MongoDB for fast retrieval.
- Implemented a hybrid ranking pipeline combining TF-IDF cosine similarity and rule-based skill-coverage to produce transparent candidate match scores and missing-skill explanations.
- Trained and compared Logistic Regression, Random Forest, and XGBoost models on a 500+ synthetic candidate dataset to evaluate ranking quality and model robustness.
- Developed a multi-page Streamlit dashboard enabling single and bulk resume analysis, candidate ranking visualization, and CSV export to accelerate screening workflows.
Education
Annamacharya Institute of Technology & Sciences, Rajampet
B.Tech — Artificial Intelligence & Data Science • 2022 – 2026
Sri Chaitanya Junior College, Vijayawada
Intermediate (MPC) • 2020 – 2022
Sri Chaitanya Techno School, Anantapur
SSC (State Board) • 2019 – 2020
Certifications
AWS Cloud Practitioner Essentials — Amazon Web Services • 2026
Advanced Machine Learning & Deep Learning — Frontlines EduTech
Data Science Certification on Emerging Technologies — APSSDC & Indo-Euro Synchronization (IES) • 2024
Achievements
- Team Lead – IEEE Research Project (India Startup Survival Predictor) — 2026: Led a team of 3 members to develop an explainable ML framework; paper published at IEEE ICETEMS 2026 (Scopus indexed).
- Top 5 – Hack-A-Tone 2025 (IIT Delhi) — 2025: Led Team BitBloom to develop an AI-powered public welfare platform, securing a Top 5 position among participating teams.
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