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Data Science with less than a year in AI/ML workflows and spatio-temporal forecasting.
Data Science graduate with a research focus in spatio-temporal forecasting, multi-source geospatial data fusion, and applied machine learning. Developed systems that integrate satellite observations, reanalysis, terrain, and ground-truth data into interpretable ML models. Secured All India Rank 6 at Smart India Hackathon 2025 (ISRO) for air quality forecasting, and built a publicly accessible policy simulation platform now in active use. Proficient in Python, SQL, XGBoost, LightGBM, ConvLSTM, Hugging Face, and cloud-based ML pipelines on AWS.
Savitribai Phule Pune University
Bachelor of Science (Honors) · Data Science
August 1, 2023 – June 30, 2027
Edunet Foundation (IBM SkillsBuild + Microsoft Elevate Program)
Artificial Intelligence & Machine Learning Intern
January 1, 2026 – March 1, 2026
India
Infosys Springboard 6.0
Data Visualization & Analytics Intern
September 1, 2025 – December 1, 2025
India
AccessLens: AI-Powered Benefit Access Risk Simulator
June 17, 2026 – Present
Built a publicly deployed full-stack platform that simulates access friction in welfare schemes, replacing eligibility checks with barrier analysis. Developed a deterministic risk model and counterfactual engine to quantify access barriers across 56 personas, 72 schemes, and 8 barrier types. Implemented a diversity-aware ranking system and an AI explanation layer that surfaces transparent policy reasoning for decision support. Deployed with multilingual support and privacy-first simulation boundaries; scalable architecture serves NGOs, policy analysts, and institutions.
SAFAL: AI-Driven Multi-Pollutant Air Quality Forecasting
June 17, 2026 – Present
Engineered a multi-source ML forecasting system for NO2 and O3 using 24-48 hour prediction windows. Processed ~1,039,390 INSAT satellite image crops and fused CPCB ground measurements, ERA5 reanalysis, and MERRA-2 aerosol products. Developed leakage-safe XGBoost models achieving PM2.5 RMSE ≈ 27.98 ± 5.34 µg/m³ and R2 ≈ 0.676. Secured All India Rank 6 at Smart India Hackathon 2025 (ISRO).
View ProjectTerrain-Aware ConvLSTM for Site-Specific Wind Speed Forecasting
June 17, 2026 – Present
Built a terrain-aware ConvLSTM framework for site-specific wind speed forecasting across 5 wind-energy sites in Maharashtra. Processed 20 TB of multi-source data on AWS, integrating 15 years of ERA5 reanalysis, WRF meteorological outputs, SRTM elevation, and ESA WorldCover land-cover layers. Designed a 5 × 5 spatial grid with 8-step lookback and 5-step autoregressive rollout to preserve local spatial structure during forecasting. Achieved MAE of 0.2042 m/s and RMSE of 0.3503 m/s over a 5-step horizon, outperforming TCN by 16.4% in MAE and 16.9% in RMSE.
Data Science Program – Relevel by Unacademy
Relevel by Unacademy
June 1, 2026 – Present
Mastering Generative AI & ChatGPT
Geeksforgeeks
June 1, 2026 – Present
Google Data Analytics Specialization
June 1, 2026 – Present
TechA Data Analytics using Power BI
TechA
June 1, 2026 – Present
Google IT Automation with Python
June 1, 2026 – Present
IBM Data Science Professional Certificate
IBM
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from welfare scheme simulation to air quality and wind speed forecasting, indicates a broad interest in applying data science to various real-world problems. Their academic background in Data Science and multiple certifications align well with a data-driven culture. The experience in both academic and internship settings, including a competitive national internship, suggests adaptability and a proactive learning attitude. The focus on interpretable ML models and transparent policy reasoning (AccessLens) also aligns with ethical AI development, which is a positive cultural indicator.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills through complex data science projects. Their involvement in hackathons and publicly deployed platforms suggests initiative and a results-oriented approach. The ability to work with diverse data sources and integrate them into ML models indicates a structured and thorough operational fit for data-intensive roles. However, as an early-career professional, further development in leadership and stakeholder communication would be beneficial.