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Data with 3+ years in IoT, Manufacturing & Healthcare Data
Data Analyst with 3.8 years of experience working across IoT, manufacturing, healthcare, and operations mostly focused on building dashboards people actually use and making sure the data behind them is clean. I have worked with Power BI (DAX, Power Query, RLS) and Tableau, written a lot of SQL across SSMS and DB2, and built Python scripts for data cleaning and basic forecasting. Some highlights: validated 27M+ IoT sensor records at 99.5% accuracy, built 10+ dashboards used by 100+ people daily, and cut report turnaround from 2 days to 4 hours by automating what used to be manual work. I am comfortable talking to stakeholders, understanding what they need, and turning that into something useful.
Rashtrasant Tukadoji Maharaj Nagpur University
Master of Science · Statistics
August 1, 2020 – June 30, 2022
SalVenturesTech
Data Analyst
August 1, 2022 – Present
Nagpur, Maharashtra, India
Healthcare Revenue & Denials Performance Analysis
August 1, 2022 – June 1, 2026
Built a 6-page Power BI report covering $360M gross and $214M net revenue across 2 years denial trends, root cause breakdown, and an executive summary page the leadership team could actually read without a data background. Queried 19,951 patient records using CTEs and window functions to pull denial patterns - found an 11.82% denial rate against an 8% industry benchmark, which became the anchor finding for the whole report. Dug into the denial patterns and found the same failure showing up across every insurance and visit type combination - collection rates stuck at 59-60% regardless. Traced it to $145.79M in revenue leakage and worked out a $13.5M Year 1 recovery scenario through appeals and auth tracking. Segmented the patient base and found 84% of patients account for 94% of revenue - helped the team focus recovery efforts on a specific high-risk group with ~$296K in recoverable annual impact.
High-Volume IoT Data Pipeline
August 1, 2022 – June 1, 2026
Built a pipeline to process 27M+ IoT records end-to-end multi-stage validation, source-to-target checks, and automated refresh set up through Power BI Service so dashboards stayed current without manual intervention. Added backend validation rules that caught bad records at ingestion - data accuracy went up by 15% and the number of pipeline anomalies we had to manually investigate dropped by 25%. Power BI dashboards fed directly from the pipeline gave ops and client teams a live view of what was happening - cut the time between something going wrong and someone noticing it by about 40%.
Environmental Monitoring & Plant Room Analytics
August 1, 2022 – June 1, 2026
Worked with sensor data from 50+ systems (temperature, CO2, humidity) - cleaning up anomalies and inconsistencies before they could cause problems downstream. Brought pipeline accuracy up by 15%. Set up alerting dashboards in Power BI so the team stopped relying on manual checks - monitoring time dropped by 60% and when something did go wrong, people found out much faster (response time down 45%). Looked at HVAC scheduling patterns and found some easy wins adjusting run times based on actual usage data - cut energy consumption by 12%.
IBM Data Analytics Professional Certificate
IBM
June 1, 2026 – Present
IBM Data Visualization with Python Certificate
IBM
June 1, 2026 – Present
PwC Switzerland Power BI Job Simulation (Forage)
PwC Switzerland
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity (healthcare, IoT, environmental monitoring) and experience in various business functions (Operations, HR, Sales) suggest adaptability and a broad understanding of different business contexts. Their focus on delivering tangible business impact (e.g., $13.5M recovery scenario, 12% energy reduction) aligns with a results-oriented culture. The Master's in Statistics provides a strong theoretical foundation for data roles.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through identifying revenue leakage and optimizing energy consumption. Their ability to translate vague requirements into working dashboards indicates good stakeholder communication and operational understanding. The focus on automation and data quality suggests a proactive and efficient work attitude.