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Data Analyst with less than a year in ETL pipelines & ML models, skilled in Power BI & SQL.
Results-oriented Data Analyst with proven experience building ETL pipelines, deploying machine learning models, and delivering BI dashboards that replace manual workflows. Ranked 8th (Top 5%) among 150+ participants at the Imarticus Data Science Hackathon. Founder and sole architect of Resilytics (resilytics.in) a live, deployed B2B SaaS platform that automates data validation, anomaly detection, and risk scoring for SMBs demonstrating rare ability to take data products from concept to production. Adept at translating complex datasets into clear, decision-ready insights for business stakeholders.
Imarticus Learning
Post Graduate Program · Data Science & Analytics
August 1, 2025 – June 30, 2026
KIT's College of Engineering, Kolhapur
B.Tech · AI & ML
August 1, 2021 – June 30, 2025
Jawahar Navodaya Vidyalaya, Ratnagiri
Class XII
N/A – May 31, 2021
Jawahar Navodaya Vidyalaya, Ratnagiri
Class X
N/A – May 31, 2019
Labmentix Pvt. Ltd.
Data Analyst Intern
February 1, 2025 – May 1, 2025
India
Resilytics - Business Intelligence SaaS Platform
June 19, 2026 – Present
Designed full end-to-end data pipeline: CSV/Excel ingestion → automated validation & cleaning → PostgreSQL star-schema warehouse → analysis engine → anomaly & risk detection → interactive insight dashboard. Applied industry-standard financial formulas: HHI (US DOJ market concentration), Basel III CV + VaR-95 (risk), modified Altman Z-Score (bankruptcy risk), and a16z Revenue Quality Score making outputs enterprise-credible. Independently managed complete cloud deployment: FastAPI backend on Railway, React/Vite/TypeScript frontend on Vercel—full production ownership.
View ProjectCustomer Churn Prediction System
June 19, 2026 – Present
End-to-end ML pipeline: EDA, feature engineering, hyperparameter tuning, XGBoost classification achieving 85% accuracy on 7,000+ telecom records; churn drivers visualised in Power BI for stakeholder consumption.
View ProjectE-Commerce Business Analytics
June 19, 2026 – Present
Analysed 100,000+ orders across 9 relational tables using advanced SQL (CTES, Window Functions: RANK, LAG, NTILE, Stored Procedures, multi-table JOINs). Delivered RFM customer segmentation (Champions to Lost), Pareto revenue analysis (top 5 product categories = 62% of total revenue), and cohort retention table. Built Power BI drill-through dashboard revealing delivery delay root causes.
View ProjectRetail Sales Intelligence Platform
June 19, 2026 – Present
Integrated Python, SQL, and Power BI into a unified sales-performance analytics platform tracking revenue growth and product category trends to support demand forecasting.
View ProjectData Analysis with Python
Forage
June 1, 2026 – Present
ML & NLP Bootcamp
Udemy
June 1, 2026 – Present
8th Rank (Top 5%)
Imarticus Data Science Hackathon
April 1, 2026 – Present
ML & AI Workshop
IIT Varanasi
February 1, 2023 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from personal SaaS development to academic projects in retail and e-commerce analytics, indicates a broad interest and adaptability. Their proactive approach to learning and applying advanced concepts (e.g., financial formulas, ML pipelines) suggests a growth mindset. The independent development and deployment of 'Resilytics' highlight a strong entrepreneurial spirit and self-motivation, which can be a valuable cultural asset. Their academic background in AI & ML, coupled with a Post Graduate Program in Data Science & Analytics, shows a commitment to continuous learning and staying current with industry trends.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, evidenced by their hackathon performance and the complexity of their personal project (Resilytics). Their ability to manage a full cloud deployment independently suggests a high degree of initiative and ownership. Collaboration with teams to align KPI definitions indicates good teamwork and communication for operational fit. The focus on reducing manual effort and saving time through automation aligns well with efficiency-driven operational environments.