Darshit Jayswal — Data & Business Analyst, 8 years turning messy business data into clear, money-relevant decisions with Power BI, SQL and Python.
I started in market research asking why customers behave the way they do — and now answer it with Power BI dashboards, SQL, and Python. My path from primary research to BI gives me an edge most analysts don't have: I understand the business question behind the data request.
At GJ Tech Solutions, I designed acquisition funnel dashboards tracking the complete player journey (lead → KYC → first deposit), connected Power BI directly to Salesforce and Amazon Athena via ODBC to eliminate manual exports, and delivered competitive intelligence that contributed to 20% YoY growth. I also led product research that shaped the roadmap.
Ahmedabad-based. Open to Data / Business / Marketing / BI Analyst & Power BI Developer roles.
End-to-end 3-page Power BI dashboard analysing 7,043 subscribers with dimensional modelling, DAX time-intelligence, and RFM segmentation. 45.8% of churn occurs within the first 90 days. 8 SQL queries covering cohort retention and contract-tier churn (Monthly 29.5% vs Two-Year 5.9%).
Analysed the 2026 tech layoff wave using real data from layoffs.fyi (2020–2026). 3-page Power BI dashboard covering yearly trends, industry/country breakdowns, and an AI-attribution lens separating AI-cited layoffs from broader cost-cutting. DAX measures include YoY % change and rolling aggregations.
End-to-end EDA of 10,000+ retail transactions across 4 U.S. regions. Decomposed sales performance by category, sub-category, and customer segment. 8 production-quality visualisations covering regional share, category margin, time-series trends, and sub-category profit deep-dives using Matplotlib, Seaborn, and Plotly.
Full acquisition funnel analysis (Lead → Signup → Verified → Paid) across 4 channels with CAC and ROI comparison. Cohort retention heatmap identifying Month 1 as the critical drop-off point. MRR trend by traffic source, regional breakdown, and interactive HTML dashboard with 6 Plotly charts.
Rigorous A/B test analysis for an e-commerce checkout redesign across 10,000 users. Control CVR 3.5% vs Variant 4.9%. Chi-Square confirms statistical significance (p < 0.0001). Includes Wilson confidence intervals, Independent T-Test on revenue per user, Mann-Whitney U on session duration, device segmentation, and annual revenue impact model projecting +$970K/year uplift.
Open to Data / Business / Marketing / BI Analyst
& Power BI Developer roles.
Let's talk about turning your data into decisions.
ahmedabad, india · +91 95744 98784