KIRAN R
Jr. Data Scientist • Banglore, India • k************@gmail.com • +91*******683 • github.com/••••• • tinyurl.com/•••••
Professional Summary
Jr. Data Scientist with 0 years of experience proficient in Python, SQL, and machine learning. Hands-on experience in predictive modeling, time-series forecasting, feature engineering, model validation, and deploying interactive dashboards. Skilled at deriving business insights from large datasets and communicating results via Tableau and Streamlit for stakeholder decision-making.
Technical Skills
Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy,SciPy,Scikit-learn,Matplotlib,Seaborn
Databases: SQL,MySQL,SQL querying
Data and Analytics: XGBoost,Hyperparameter tuning,Regression,Classification,Feature engineering,Plotly,Tableau,Streamlit
Tools and Methodologies: Jupyter Notebook,Google Colab,PyCharm,Git,GitHub
Time Series & Forecasting: Prophet,TimeSeriesSplit,Lag & rolling features,Forecast evaluation MAPE
Mathematics & Statistics: Probability,Statistics,Linear algebra
Work Experience
Ai Variant
Banglore, India
Data Science Intern
08/2025
Supported AI/ML model development and deployment at Ai Variant, working on data preprocessing, predictive modeling, and stakeholder-facing reporting for business use cases.
Tech Stack: Python, Pandas, Scikit-learn, XGBoost, Prophet, TimeSeriesSplit, Streamlit, Tableau, Jupyter Notebook, MySQL, GitHub
- Performed data cleaning and preprocessing on large, multi-source datasets using Python and Pandas to create a reliable feature store for downstream modeling and analysis.
- Conducted exploratory data analysis (EDA) and visualization with Seaborn and Plotly to identify trends, seasonality, and data quality issues communicated to stakeholders via Tableau-ready reports.
- Engineered time-series and cross-sectional features (lags, rolling statistics, calendar encodings) and built feature pipelines to improve model input stability for forecasting tasks.
- Trained and validated predictive models using Scikit-learn and XGBoost with cross-validation strategies (including TimeSeriesSplit) to guard against leakage and measure generalization.
- Optimized model performance through hyperparameter tuning (GridSearchCV/RandomizedSearchCV) and model evaluation workflows, documenting experiments and model selection rationale for reproducibility.
- Packaged prototype outputs and delivered interactive dashboards using Streamlit with Tableau export integration to enable business users to explore forecasts and model insights.
Projects
Demand Forecasting & Inventory Optimization | 04/2026 – 06/2026
Tools Used: XGBoost, Prophet, Feature engineering, TimeSeriesSplit, Streamlit, Tableau, Inventory optimization EOQ
- Built an end-to-end demand forecasting pipeline using XGBoost and Prophet on 1M+ retail sales records across 1,115 stores, achieving 11.2% MAPE vs. a 31.4% naive baseline (64% improvement).
- Engineered 23 time-series features including lag values, rolling statistics, and cylindrical calendar encodings to capture temporal patterns and seasonality.
- Used TimeSeriesSplit cross-validation and careful leakage controls to validate model performance across stores and product hierarchies.
- Designed an inventory optimization module (EOQ, safety stock, reorder point) driven by forecast outputs that reduced simulated stockout rate from 14.3% to 5.8%.
- Deployed an interactive Streamlit dashboard with Tableau-integrated exports to enable business stakeholders to review forecasts and inventory recommendations.
Hybrid Stock Forecasting | 10/2025 – 10/2025
Tools Used: Hybrid modeling, Time-series analysis, Model evaluation
- Created hybrid forecasting models that combined statistical methods and machine learning to capture both trend/seasonality and non-linear patterns in financial time series.
- Conducted time-series analysis to uncover recurring patterns and anomaly signatures to inform model architecture and feature selection.
- Improved overall prediction accuracy by 80% compared to baseline statistical models through ensemble and stacked approaches.
- Implemented model evaluation pipelines to compare statistical and ML-based forecasts using holdout periods and error metrics.
- Produced clear visualizations of forecast components and uncertainty bands for investment insight communication.
Education
Government Engineering College
BE (Computer Science and Engineering) • Chamarajnagara, India • 12/2021 – 05/2025
Certifications
Data Science Certification — ExcelR Solutions • Jul 2025
British Airways - Data Science Job Simulation — Forage
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