Data Analyst

Akhil Vidiyala

Arlington, VA +1-857-***-**** ***********************@*****.*** LinkedIn

(Open to Relocate)

PROFESSIONAL SUMMARY

Results-driven SQL Developer with 3+ years of experience delivering data-driven insights across healthcare, e-commerce, telecom sectors. Proven track record in building predictive models, real-time dashboards, and customer segmentation strategies using Python, SQL, Tableau and Power BI. Strong collaborator with cross-functional teams to drive operational efficiency, customer retention, and revenue growth. Actively seeking full-time opportunities in data analytics and business intelligence roles. AWARDS AND CERTIFICATIONS

● SAP Analytics Cloud - Data Analyst Certification

● AWS Certified Developer Associate

WORK EXPERIENCE

ANAND PAG INC Remote, US

SQL Developer May 2024 - Present

Client: Southern California Edison

Designed predictive analytics solutions to identify equipment failure patterns and streamline sales operations through real-time dashboards and cross-system data integration.

● Designed an interactive Order-to-Cash dashboard in Power BI, integrating Salesforce and SAP data to track funnel drop-offs, accelerating deal closures by 20% and improving quarterly cash flow forecasting

● Developed and executed complex SQL queries and Python/R scripts for data extraction, transformation, and analysis, leading to actionable insights that drove business decisions.

● Created dynamic Tableau dashboards for revenue trends, cost centers, and budget vs. actuals to improve FP&A visibility for leadership.

● Parsed and analyzed 100K+ SAP workflow logs using SQL to identify process bottlenecks, contributing to a 15% reduction in cycle time and an 18% improvement in SLA compliance

● Built a real-time incident prediction pipeline in SAP HANA, analyzing equipment failure logs and IoT sensor data to generate early warnings, improving forecast accuracy by 35% and reducing downtime by 22%

● Standardized KPI definitions across Finance, Sales, and Operations, creating a unified semantic layer in Power BI that eliminated reporting discrepancies and reduced delays by 70%

● Optimized SQL queries with window functions and CTEs to calculate rolling averages, approval trends, and funnel conversion KPIs, improving dashboard refresh performance by 30%

● Partnered with SAP, Salesforce, and cross-functional teams to deploy scalable analytics solutions, driving higher adoption and a 4x increase in dashboard usage across departments TATA CONSULTANCY SERVICES Hyderabad, India

SQL Developer Jul 2022- Aug 2023

Client: Airtel Telecom

Consolidated and analyzed 1M+ customer records using Python, SQL, and Power BI to uncover churn drivers, create KPI dashboards, and deliver actionable retention insights that reduced churn by 12%

● Cleaned and standardized 1M+ customer records using Python & SQL, building a reliable dataset with attributes like usage, tenure, and complaints for churn analysis

● Performed exploratory data analysis (EDA) in Python (Pandas, Matplotlib) and SQL to uncover churn drivers across tenure, payment method, and complaint frequency, helping business teams prioritize retention levers

● Designed SQL-based KPI reports and Power BI dashboards to track churn rate trends, customer lifetime value (CLV), and retention metrics by region and plan type, improving visibility for managers

● Streamlined SQL workflows with window functions to calculate rolling averages, churn rates, and retention cohorts, reducing query runtime by 30% and improving dashboard refresh speed

● Partnered with marketing team to run A/B tests on targeted retention campaigns, reducing churn by 12% and increasing campaign ROI by 15%

Client: FMCG Enterprise (Unilever)

Analyzed multi-year sales data from SAP and Excel to deliver SKU-level demand insights, improve inventory planning, and reduce supply chain inefficiencies through SQL automation and interactive dashboards

● Aggregated and cleaned 3+ years of sales data from SAP exports and Excel across 50+ SKUs and multiple regions, creating a reliable dataset for demand planning

● Performed exploratory sales trend analysis using SQL and Python to identify demand variability by SKU, region, and promotion cycle, helping planners anticipate stock risks

● Developed Power BI dashboards with SQL joins to visualize SKU-level forecasts, inventory KPIs (MAPE, stockouts, overstock rates), and safety stock buffers, providing actionable insights to supply chain managers

● Refined SQL scripts using window functions and CTEs to calculate rolling averages, promotion uplift, and seasonal demand indices, reducing query runtime by 35% and ensuring faster dashboard refreshes

● Conducted A/B testing on forecast approaches (ARIMA vs Prophet) to validate performance improvements, enabling planners to select the most accurate method per SKU and improve forecast reliability

● Delivered real-time SKU-level demand insights that enabled data-driven inventory planning, resulting in a 15% reduction in surplus stock and a 21% improvement in on-shelf availability during the holiday season Client: E-Commerce (Croma)

Optimized revenue per marketing rupee by performing behavioral segmentation and identifying high-impact customer cohorts for targeted campaigns.

● Extracted customer transaction data from SAP, Excel, and web logs and stored it in a SQL database, creating a centralized source for repeatable analysis

● Built Power BI dashboards connected to SQL tables, enabling marketing teams to explore cohorts, track campaign KPIs (CTR, conversion, revenue per customer), and reuse segmentation outputs

● Automated KPI and cohort calculation workflows in Python, eliminating Excel-based manual reporting and reducing effort by 90%, while improving accuracy and consistency across campaigns

● Performed RFM analysis and applied K-Means clustering in Python to segment customers into personas such as “Frequent Loyalists” and “One-Time High Spenders

● Translated segmentation outputs into actionable KPIs for marketing, supporting targeted campaigns that achieved a 15% lift in CTR and delivered 11–17x return on spend TRANSGRAPH Hyderabad, India

Business Intelligence Analyst Sep 2021-Jun 2022

Client: Nutralife Oils & Agro

Consolidated and analyzed multi-source procurement data (commodity prices, rainfall, currency rates) using SQL, Python, and Power BI to deliver actionable insights, improve forecast reliability, and optimize procurement decisions

● Collected and cleaned 2M+ multi-source records (commodity prices, rainfall, currency rates) using SQL and Pandas, building a centralized dataset for procurement analysis

● Developed forecasting workflows (ARIMA, Prophet, XGBoost) enriched with macroeconomic signals

(Brent Crude, INR/USD, rainfall), achieving less than 10% MAPE during volatile periods

● Built interactive Power BI dashboards with 7-day and 30-day forecast curves, volatility bands, and procurement signals (“Buy Now”, “Hold”), reducing manual monitoring effort by 70%

● Conducted lag correlation analysis to quantify delayed impacts of macroeconomic factors on commodity prices, improving directional forecast accuracy to 88%

● Partnered with CFOs and procurement teams to deploy a confidence-based alerting system, boosting ROI from forward contracts by 20% and improving procurement efficiency by 22% PROJECTS

Chronic Diseases Risk Prediction

● Analyzed 445,000+ records from the CDC’s BRFSS dataset using advanced EDA and K-Means clustering to identify high-risk demographic groups and uncover regional health trends.

● Implemented classification models, including XGBoost, logistic regression, and random forest via Scikit-learn, improving prediction accuracy by 20%

● Applied robust feature engineering techniques (categorical encoding, sequential feature selection), boosting model interpretability by 15% to support actionable, data-driven healthcare strategies SKILLS

● Databases: PostgreSQL,Microsoft SQL Server, MySQL

● Programming Languages: Python (NumPy, Pandas, Matplotlib, Scikit-learn, PyTorch), R (Shiny, dplyr, ggplot2), SQL

● Tools & Libraries: Advanced Excel, PowerPoint, JIRA

● Data Visualization: Power BI(Power Query, DAX),Tableau, SAP Analytics Cloud, DOMO

● Data Analytics and modeling: Predictive Modeling, Time Series Forecasting (ARIMA, Prophet), RFM Analysis, Clustering (K-Means), A/B Testing, Hypothesis Testing, Feature Engineering, Model Evaluation

(MAPE, Accuracy)

EDUCATION

NORTHEASTERN UNIVERSITY Boston, MA

MS in Business Analytics(GPA: 3.67/4)Sep 2023 - Dec 2024 VNR VJIET Hyderabad, India

Bachelor of Engineering in Information Technology Jul 2018 - Jun 2022

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