Snapshot
Gear
Project & Program Management
Waterfall, Agile, Project Control, Cross-functional Leadership, Stakeholder & Risk Management, Decision Making
Balaji Raj Veluchamy
Project Manager & Business Analyst
Project Management • Business Intelligence • Data Analytics • Machine Learning
I drive data-informed strategy by integrating business intelligence, advanced analytics, and machine learning insights, collaborating cross-functionally to translate data into measurable business outcomes.
Excel
PowerPoint
Power BI
ETL
Tableau
Python
SQL
ML
AI
Snapshot
Who I Am
Profile
I’m a Project Manager & Business Analytics professional with a Master’s in Business Analytics from Arizona State University, passionate about using data to inform better decisions. I have built predictive models, designed analytical dashboards, and applied statistical methods to uncover meaningful patterns. I thrive in roles that sit between data and business, working with stakeholders to translate complex analysis into insights that drive strategy, efficiency, and impact.
My experience spans project management, data analysis, and business analytics in consulting projects and process optimization. I am passionate about leveraging data to solve business challenges and drive decision-making.
Education
Arizona State University | 2024
Master of Science, Business Analytics
Focused on Business data mining, modeling, analytics, visualization and machine learning with applied projects.
Supply Chain Analytics - Capstone project, reducing excess inventory and improving approval process.
Sri Venkateswara College of Engineering, Anna University | 2019
Bachelor of Automobile Engineering
Explored Engineering Mechanics and Part Design & Analysis.
Built All-Terrain Vehicle for National BAJA Competition through design, fabrication, testing, and assembly.
Gear
Core Competencies
Operational Intelligence
Lean Six Sigma, DMAIC, Root Cause Analysis, KPI Monitoring, Continuous Improvement, Process Optimization
Data Analysis & Visualization
Excel (Advanced), SQL, Power BI, ETL, Data Modeling, Dashboarding, Communication, Performance Reporting
Machine Learning & AI
Python, Forecasting, scikit-learn, TensorFlow, NLP, LLMs, Prompt Engineering, Computer Vision
Timeline
Timeline
Continuous Learning Timeline
A strategic trajectory from technical foundations to AI-driven leadership.
2019
Technical Assistant
Excel, PowerPoint, Word
2020
Business Intelligence
ETL, Feasibility Study
2021
Project Coordinator
Continuous Improvement, Kaizen
2022
Data Analytics
Power BI, Python
2023
Assistant Project Manager
Lean Six Sigma, OpEx
2024
Business Analytics
Machine Learning, Optimization
2025
AI Data Analyst
Tutoring, Prompt Engineering
2026
Project Manager / BA
PMP, Deep Learning
Future Plan
AI Business Architect
Sr. Program Manager
Trail
Where I've Worked
AI Data Analyst
- 91% Data Quality: Analyzed large-scale human-labeled datasets using RLHF and supervised fine-tuning techniques, improving model performance and achieving 91% data quality across evaluation metrics.
- 12 AI Projects: Contributed to multimodal data initiatives for 12 AI projects, including image, audio, video datasets, by rigorous research and adhere to guidelines.
- Feedback Loops: Developed Word, Excel dashboards enabling real-time Quality tracking, experiment feedback loops, reporting and stakeholder communication.
- AI Prompt Engineering: Gained hands-on expertise in AI prompt engineering & image generation using generative AI models, including Gemini, Grok, and OpenAI.
Marketing Research Assistant
- R -> Python: Converted a full R-based marketing analytics curriculum into Python, building reusable notebooks to improve accessibility for graduate analytics learners.
- Research Analytics: Bridged academic research and practical analytics applications through scalable, notebook-based learning modules.
Assistant Project Manager
- 90+ Projects: Coordinated cross-functional projects end-to-end across operations to deliver strategic business values.
- Six Sigma: Applied Lean Six Sigma to optimize warehouse consumables, delivering 8 lakhs in savings and 40% movement reduction.
- Business Intelligence: Built scalable KPI dashboard and analytics frameworks using Excel, Power BI, and ETL.
- 94% Engagement: Established continuous improvement culture by training employees on 5S, kaizen, and RCA.
- Executive Reporting: Developed and presented senior management reports to achieve high-level alignment.
- S+ Performance: Achieved S+ (95%) rating in yearly appraisals by consistently exceeding annual goals.
- Digital Transform: Advanced project methodologies by practicing Lean Six Sigma, Power BI, and Python.
- Value Stream Mapping: Conducted structured process studies to identify bottlenecks and recommend data-backed improvements.
- Efficiency: Led high-impact projects in safety and industrial efficiency using KPI definition and Gemba walks.
- Process Governance: Maintained steering committee processes including SOP, audits, and change management.
- 3.5% Savings: Delivered 3.5% budget savings and improved safety metrics via continuous improvement.
- KPI Dashboards: Developed executive-ready dashboards with 98% data integrity for operational reviews.
- Standards: Standardized reporting templates and data definitions, reducing rework.
- Documentation: Supported cross-functional program documentation to enhance compliance.
- Administration: Enabled repeatable, scalable reporting processes aligned with leadership priorities.
Lab
Lab
Work in Focus
Highlighting 10 projects across operations, analytics, supply chain, and automotive vehicles that support operational and business decisions. Replace impact metrics with verified results.
Excess Inventory Optimization for Capital Efficiency
Built a lead-time-driven demand forecasting and inventory risk analytics framework integrating multi-source supply chain data to increase excess inventory risk visibility and improve decision allocation based on capital level to respective senior leaders.
Python
Excel
SQL
Power BI
Supply Chain Analytics
Parking Occupancy Detection System Using Computer Vision
Designed and deployed end-to-end computer vision analytics pipeline to automate parking space utilization detection and enable scalable infrastructure monitoring.
Computer Vision
Python
OpenCV
YOLO
RCNN
Regional Promotion Recommendation System
Built a machine learning–driven market expansion analytics model to identify high-impact regions for targeted promotional investment.
Python
Excel
Machine Learning
Power BI
Recommendation Systems
Restaurant Recommendation & Sentiment Intelligence Platform
Developed an NLP-powered analytics platform combining sentiment intelligence and recommendation modeling to enhance customer experience insights.
Python
NLP
Machine Learning
Recommendation Systems
Lean Six Sigma Transformation at Gentech (Case Study)
Led a DMAIC-driven operational transformation initiative to improve SLA performance and streamline supply chain approval workflows.
Lean Six Sigma
DMAIC
Power BI
Process Optimization
Defect Reduction Using Manufacturing Data Analytics
Built a data-driven defect detection and root cause analysis workflow using manufacturing sensor data to reduce quality losses. Delivered statistical insights and performance dashboards to support yield improvement decisions.
Python
Power BI
Statistical Analysis
Manufacturing Analytics
Operational Excellence Performance Power BI Dashboard
Designed a workforce analytics dashboard to centralize operational excellence metrics and enable leadership-level performance visibility through KPI-driven scorecards.
Power BI (DAX)
Data Modeling
Data Mining
KPI Design
Workforce Analytics
Warehouse Consumables Optimization
Led a Lean Six Sigma initiative to optimize warehouse material flow, reduce excess consumables inventory, and improve operational throughput.
Lean Six Sigma
Operations Analytics
Workplace Layout Analysis
Value Stream Mapping
Diesel Engine Emissions & Performance Optimization
Conducted experimental research to improve diesel engine combustion efficiency and reduce emissions using controlled design-of-experiments methodologies.
Design of Experiments (DOE)
Automotive Engineering
Experimental Analysis
BAJA All-Terrain Vehicle (ATV) Design & Development
Contributed to the end-to-end design, fabrication, and competitive validation of an all-terrain vehicle for the national BAJA engineering competition.
Automotive Engineering
CAD
ANSYS
Mechanical Design
Certifications
Proof of Continuous Learning
Project Management Professional (PMP)
PMI-certified in project governance, EVM, risk management, stakeholder communication, and end-to-end lifecycle delivery.
Microsoft Certified: Power BI Data Analyst Associate
Validated expertise in data modeling, DAX, dashboard development, and business intelligence reporting using Power BI.
Lean Six Sigma Yellow Belt
Certified in DMAIC methodology, process optimization, root cause analysis, and continuous improvement practices.
Contact
Let us build something together
Reach out directly
Email is the fastest way to reach me. I respond within 24 hours.
Excess Inventory Optimization for Capital Efficiency
Problem: Static 12-month forecasting models were driving excess inventory, increasing holding costs and limiting capital efficiency across supply chain operations.
Approach: Developed dynamic lead-time-based demand models using Python and SQL by integrating historical inventory, procurement, and supply data. Designed interactive Power BI dashboards to provide real-time excess inventory risk visibility and decision-level segmentation for approval workflows.
Tech Stack: Python, SQL, Power BI, Supply Chain Analytics
Results & Impact:
- Improved excess inventory risk visibility by 75% from 25%.
- Enabled structured capital approval escalation for 8% of high-value inventory requests.
- Delivered leadership-ready dashboards supporting data-backed supply chain decisions.
What I'd Improve Next: Integrate predictive forecasting (ARIMA), Automate replenishment triggers, and implement scenario simulation for demand volatility.
Operational Excellence Performance Power BI Dashboard
Problem: Lack of unified visibility into employee engagement, safety metrics, project contributions, and operational excellence initiatives limited leadership’s ability to monitor performance.
Approach: Built a real-time Power BI dashboard integrating safety, training, recognition, and improvement initiative metrics. Developed DAX-based weighted scoring models to quantify engagement and performance indicators. Structured dashboards to support daily and monthly leadership reviews.
Tech Stack: Power BI, DAX, Excel, Data Modeling, KPI Frameworks
Results & Impact:
- Enabled structured daily and monthly business reviews.
- Improved transparency across operational excellence contributions.
- Enhanced leadership decision-making through standardized KPI scorecards.
What I'd Improve Next: Incorporate predictive workforce analytics, automate data pipelines, and implement role-based dashboard access controls for scalability.
Defect Reduction Using Manufacturing Data Analytics
Problem: High defect rates in 3–4mm clear glass manufacturing were increasing production cost, reducing yield, and impacting product quality.
Approach: Cleaned and validated multi-source manufacturing sensor datasets. Conducted exploratory data analysis (EDA) and statistical correlation analysis to identify key process parameters influencing defect formation. Developed structured reporting dashboards to monitor defect trends and process variability.
Tech Stack: Python, Power BI, Statistical Analysis, Data Visualization
Results & Impact:
- Achieved 55% defect reduction, significantly improving yield and operational efficiency.
- Identified key defect drivers through data-backed correlation analysis.
- Enabled data-informed quality control decisions through interactive dashboards.
What I'd Improve Next: Integrate real-time sensor streaming, implement predictive defect modeling, and deploy automated anomaly alerts for proactive quality control.
Restaurant Recommendation & Sentiment Intelligence Platform
Problem: Restaurants lacked structured insight into customer sentiment drivers and competitive positioning from large-scale review data.
Approach: Performed NLP-based sentiment analysis and topic modeling (LDA) using spaCy and Gensim. Built recommendation engines using cosine similarity and collaborative filtering to personalize restaurant discovery.
Tech Stack: Python, spaCy, scikit-learn, Gensim, NLP
Results & Impact:
- Identified key drivers of customer satisfaction through sentiment clustering.
- Delivered personalized restaurant recommendation framework.
- Enabled structured competitive positioning insights.
What I'd Improve Next: Integrate real-time review ingestion, deploy transformer-based NLP models, and incorporate location-aware ranking systems.
Parking Occupancy Detection System Using Computer Vision
Problem: Manual parking utilization tracking lacked scalability, real-time accuracy, and data-driven capacity planning support.
Approach: Built a full computer vision workflow using ROI-based image segmentation and object detection models (YOLO, RCNN). Processed 200+ labeled images with spatial validation techniques to minimize false positives and ensure reliable occupancy classification.
Tech Stack: Python, OpenCV, YOLO, RCNN
Results & Impact:
- Achieved 90% classification accuracy across validation datasets
- Enabled automated, scalable occupancy monitoring
- Demonstrated feasibility of AI-driven smart infrastructure analytics
What I'd Improve Next: Deploy live camera integration, optimize inference latency, and implement edge-based real-time processing.
Strategic Regional Promotion Recommendation System
Problem: Promotional investments were deployed without structured regional similarity analysis, limiting scalability beyond Saint Petersburg and increasing risk in expansion decisions across 85 geographically diverse regions.
Approach: Analyzed 19 years of per-capita alcohol consumption data (1,615 samples, 85 regions). Cleaned and optimized dataset (reduced to 81 valid regions after removing incomplete records). Conducted Time-series trend analysis, Correlation analysis, and Geographic enrichment using latitude/longitude clustering. Applied multi-model similarity validation using Hierarchical Clustering, Cosine Similarity, Collaborative Filtering, and DBSCAN clustering.
Tech Stack: Python, scikit-learn, Power BI, Machine Learning
Results & Impact:
- Identified 3 consistently similar regions across all similarity models: Moscow, Khabarovsk Krai, and Ivanovo Oblast.
- Reduced expansion uncertainty through multi-model validation.
- Provided data-backed framework for cost-efficient campaign replication.
What I'd Improve Next: Add time-series forecasting for seasonal consumption modeling. Implement uplift modeling to estimate true promotional impact. Incorporate demographic & economic indicators for deeper segmentation.
Lean Six Sigma Transformation at Gentech (Case Study)
Problem: Service Level Agreement (SLA) performance at 72.3% indicated process inefficiencies across proposal creation and supplier-to-customer approval workflows.
Approach: Applied Lean Six Sigma DMAIC methodology, conducted root cause analysis (5 Whys), and performed structured data analysis using Excel and Power BI to identify workflow bottlenecks. Implemented process standardization and governance improvements.
Tech Stack: Excel, Power BI, Lean Six Sigma, DMAIC
Results & Impact:
- Improved SLA performance from 72.3% to 89% (16.7% increase).
- Streamlined approval workflows and reduced service delays.
- Strengthened supply chain performance visibility through visual dashboards.
What I'd Improve Next: Implement automation in proposal routing, introduce real-time SLA monitoring dashboards, and integrate predictive risk alerts.
Warehouse Consumables Optimization
Problem: Excess packaging material inventory and inefficient warehouse movement were increasing operational costs and reducing material handling efficiency.
Approach: Conducted workplace layout analysis and value stream mapping (VSM) to identify non-value-added activities. Applied Lean Six Sigma methodologies and root cause analysis to redesign layout and optimize procurement controls.
Tech Stack: Lean Six Sigma, DMAIC, Excel, Value Stream Mapping, Operational Analytics
Results & Impact:
- Delivered 50% annual cost savings in consumables.
- Reduced forklift movement by 40%, improving throughput and safety.
- Standardized process controls to sustain operational improvements.
What I'd Improve Next: Deploy inventory forecasting models and implement real-time warehouse performance dashboards to enhance monitoring and demand alignment.
Diesel Engine Emissions & Performance Optimization
Problem: Diesel engines required improved combustion efficiency while reducing fuel consumption and emissions.
Approach: Performed experimental DOE using Kirloskar diesel engines to analyze the impact of magnetic field variations on fuel injection and combustion characteristics. Measured heat rates, fuel efficiency, and emission levels under controlled testing conditions.
Tech Stack: Design of Experiments (DOE), Experimental Analysis, Statistical Process Control
Results & Impact:
- Demonstrated improved combustion heat rates.
- Reduced fuel consumption under tested conditions.
- Observed lower emission output compared to baseline tests.
What I'd Improve Next: Expand testing across broader engine loads, integrate advanced combustion analytics, and validate results under real-world driving scenarios.
BAJA All-Terrain Vehicle (ATV) Design & Development
Problem: Design and build a fully functional all-terrain vehicle capable of passing national BAJA competition technical and performance tests.
Approach: Participated in vehicle design, fabrication, assembly, and performance validation. Conducted component-level testing including brake systems, suspension analysis, maneuverability evaluation, and engine performance assessment. Managed procurement and bill of materials (BOM) coordination.
Tech Stack: CAD, ANSYS, Mechanical Design, Fabrication & Manufacturing
Results & Impact:
- Successfully passed brake, suspension, maneuverability, engine, and design validation tests.
- Delivered a competition-ready ATV capable of completing race trials.
- Gained hands-on experience in full lifecycle product development and testing.
What I'd Improve Next: Optimize weight distribution using simulation-driven design, improve suspension tuning, and enhance performance modeling for competitive advantage.
Project Management Professional (PMP)
Microsoft Certified: Power BI Data Analyst Associate
Lean Six Sigma Yellow Belt
