Bhavani Siva Jyothi Pinniboina
Logistics Analyst @Ryder System, Inc.
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WORK HISTORY
Logistics Analyst @Ryder System, Inc.
FL, US
Built a freight cost variance framework in SQL Server and Python (Pandas) to reconcile ˜14K monthly carrier invoices against rate cards, recovering $1.2M in overbilled freight charges.Engineered Airflow ETL pipelines ingesting EDI carrier invoice data from TMS platforms into Snowflake, reducing manual reconciliation effort by 65% and enabling near-real-time cost visibility.Developed a Power BI carrier scorecard tracking 12 KPIs (on-time delivery, invoice accuracy, tender acceptance) across 38 partners, supporting VP-level quarterly business reviews.Performed lane-level regression analysis in Python to isolate fuel surcharge and accessorial anomalies, driving an 8.4% per-shipment cost reduction through targeted carrier renegotiations.Partnered with Finance and Carrier Relations to model rate-change impacts across 200+ freight lanes, delivering Excel scenario analysis used in executive strategy planning.Automated monthly SLA compliance and freight spend reporting via Python and Airflow, reducing turnaround from 5 days to same-day and saving 18 analyst hours weekly.
EDUCATION
Sri Chaitanya College of Education
Intermediate , MPC
Florida Atlantic University
Master's degree, Data Science and Analytics
Sri Chaitanya Techno School
SSC
Bapatla Engineering College
Bachelor of Technology - BTech, Electrical, Electronics and Communications Engineering
ABOUT BHAVANI SIVA JYOTHI PINNIBOINA
Data Analyst with 3+ years of experience turning complex data into actionable insights across finance, healthcare, and HR domains. I specialize in building end-to-end data pipelines, developing predictive and credit risk models, and delivering clear, decision-ready dashboards for business and leadership teams.Currently at MetLife, I work on large-scale HR and benefits data, where I design ETL pipelines using Python, SQL, and AWS to support analytics for 1M+ employees. I collaborate closely with data scientists and product teams to build predictive models and run A/B tests that improve benefit adoption, engagement, and operational efficiency. My work has helped reduce manual reconciliation, improve reporting accuracy, and enable data-driven personalization.Previously at DMI Finance, I focused on credit risk and lending analytics—building and validating scoring models, engineering features from structured and alternative data, and deploying models into production systems. These solutions improved predictive accuracy, reduced NPAs, and accelerated loan decisioning.I’m hands-on with Python, SQL, Tableau, Power BI, cloud platforms (AWS/GCP/Azure), ETL tools, and machine learning techniques, and I enjoy working at the intersection of analytics, business, and technology.I’m passionate about solving real-world problems with data, collaborating across teams, and continuously learning. Always open to discussions around Data Analyst, Analytics, or Business Intelligence opportunities.
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