Mahesh Sriram

Data Analyst and 2026 Computer Science graduate with 6+ months of internship experience building analytics solutions across healthcare, sales, finance, and e-commerce domains.

Delivered

Power BI dashboards, automated ETL pipelines, and predictive models that drove measurable business outcomes for clients including Infosys. Proficient in advanced SQL, Python, Power BI (DAX/Power Query), and Azure cloud data engineering (ADF, Databricks, Delta Lake).

Certified Databricks Data Engineer

Associate (2026). Adept at translating stakeholder requirements into production-ready data solutions.

Data Analyst Intern at Human Initials (2025-07 – 2026-02)

• Delivered Power BI dashboards using advanced DAX and Power Query across 4 business verticals (sales, e-commerce, healthcare, finance), reducing reporting cycle time by 35% and enabling real-time KPI visibility for senior leadership.
• Automated end-to-end SQL and Python data pipelines for large-scale datasets, cutting manual data preparation time by 70% and establishing a single source of truth across 3+ departments.
• Gathered requirements from business stakeholders and iterated on dashboard designs until 100% adoption within 4 weeks, replacing estimate-based decisions with live data.
• Engineered 15+ complex DAX measures including running totals, period-over-period comparisons, and conditional KPIs, deployed to Power BI Service with scheduled refresh, saving 4+ hours of manual reporting overhead per week.

Data Science Intern at Infosys (2024-11 – 2025-02)

• Built Power BI dashboards tracking patient outcomes, symptom trends, and operational KPIs for a telemedicine platform, improving healthcare decision-making efficiency by 25% and reducing management reporting time by 60%.
• Developed Python predictive models for patient follow-up forecasting using feature engineering and statistical modelling, achieving 85% accuracy across 500+ patient records.
• Integrated Google Calendar API to automate scheduling workflows, reducing coordination overhead by 40% and eliminating 90% of scheduling errors.
• Presented model outputs to 10+ stakeholders, translating technical findings into recommendations that directly influenced 3 operational process improvements.

B.Tech in Computer Science and Engineering – KKR & KSR Institute of Technology and Sciences (2022-08 – 2026-04)