Analyst at

Early-career builder preparing for professional work

I am starting my career and focused on learning practical skills, building projects, and preparing for my first professional role.

View my work Message Me

Work samples

A portfolio of projects and exercises that demonstrate the skills I am developing and the practical work I have completed while preparing to enter the workforce.

Plant Assignment Optimization Using Mixed-Integer Programming | Descriptive & Diagnostic Analytics
Plant Assignment Optimization Using Mixed-Integer Programming | Descriptive & Diagnostic Analytics
Built a Generalized Assignment Problem (mixed-integer linear program) in Python (PuLP/CBC) to optimize plant-order routing across 9,215 real orders and 19 manufacturing plants; found a $146K (0.9%) cost-reduction opportunity while enforcing real capacity, product-eligibility, and contractual (VMI) constraints. Conducted Pareto/ABC concentration, delivery-performance, carrier scorecard, and capacity-utilization analysis using pandas and matplotlib, surfacing operational insights (e.g., 63% of volume concentrated in 5 of 46 customers) beyond the core optimization.
PythonPuLPCBCpandasmatplotlib
Business Analytics Practicum | Strategy & Consulting – MSBDC | UMass Amherst
Business Analytics Practicum | Strategy & Consulting – MSBDC | UMass Amherst
Performed EDA on MSBDC client, sales, and trade data across 14+ counties (FY22–FY25); mapped international trade activity by country to identify Europe as the dominant export market for MSBDC-assisted businesses, ahead of Canada and Asia-Pacific. Segmented clients by industry and consultation pattern, uncovering a mismatch: MSBDC directed the most consultation hours toward service-based clients, while product-based businesses showed faster export growth in Europe despite receiving comparatively less support. Recommended reallocating consultation resources toward these higher-performing client segments, giving MSBDC leadership data to strengthen funding and policy conversations with state and federal partners on small business investment.
Supply Chain Analytics | Geospatial Optimization – Amazon Prime Air
Supply Chain Analytics | Geospatial Optimization – Amazon Prime Air
Designed a 3-hub drone fulfillment network across 14 Massachusetts counties serving 5M+ residents by applying Center of Gravity and Huff Gravity models to demographic, e-commerce, delivery-distance, package-weight, weather, and bandwidth data to site candidate drone hub locations, validating coordinates against Google Earth. Identified Newton hub as capturing 72.35% of statewide demand (4,926 daily deliveries within a 5-mile radius) versus Worcester (1,068) and Springfield (1,020), informing lead-time and inventory-allocation strategy. Recommended Newton as the primary hub, with Worcester as a secondary option, delivering a phased deployment roadmap and executive memo covering procurement trade-offs, rollout-sequencing cost savings, regulatory constraints (FAA BVLOS), and open risks of drone range/endurance and real-time data availability for leadership to resolve before a full state rollout.
Center of Gravity modelHuff Gravity modelGoogle Earth
Pricing Intelligence Model – Airbnb (New Orleans)
Pricing Intelligence Model – Airbnb (New Orleans)
Preprocessed 7,444+ Airbnb listings using IQR-based outlier detection; engineered 10+ features including amenity count, host tenure, availability, and guest capacity. Identified bathrooms as the strongest price driver (44% feature importance) via an optimized Random Forest model achieving R² = 0.58 and 86.6% of predictions within $100 of actual price. Uncovered neighborhood-level pricing disparities (French Quarter average: $5,660 vs. citywide median: $111), informing host pricing strategy and platform dynamic-pricing recommendations.
Random ForestIQRfeature engineering
Machine Learning | Model Benchmarking – Credit Card Default Prediction
Machine Learning | Model Benchmarking – Credit Card Default Prediction
Benchmarked Logistic Regression (AUC 0.772), Classification Tree (AUC 0.760), and Neural Network (AUC 0.696) across 25+ architectures on 10,000 customer records for credit-default prediction. Performed EDA, data cleaning, dummy-variable creation, tree pruning, and lift-chart analysis to optimize model cutoff thresholds (0.26 selected for best F1 = 0.542). Recommended Logistic Regression for production deployment based on superior F1 score and regulatory interpretability, with a three-tier intervention strategy (high / medium / low risk).
Logistic RegressionClassification TreeNeural Network
Statistical Analysis – Hybrid Work Adoption Among U.S. Employers
Statistical Analysis – Hybrid Work Adoption Among U.S. Employers
Tested the hypothesis that >50% of U.S. companies have permanently adopted hybrid work by 2026, using a systematically constructed sample of 29 companies across industries (IT, Finance, Retail, Manufacturing, Health Services, Hospitality, Government, Education). Observed that the hybrid rate of 62.1% did not reach statistical significance at α = 0.05 (z = 1.30, p = 0.097); 95% Wilson CI [0.44, 0.77] spanned the null boundary. Key takeaway being conclusion is industry conditional, IT industry reached almost 80% hybrid while the other industries were significantly lower.

About me

I am starting my career and focused on learning practical skills, building projects, and preparing for my first professional role.

I am Vyshnavi Siddula. I am at the start of my career, with limited formal work experience and a focus on learning and building practical skills as I prepare for professional opportunities.

Experience

Marketing Intern | Hyderabad, India

Confederation of Indian Industry – Indian Green Building Council (CII-IGBC) · Jun 2023 – Aug 2023

Human Resources Intern | Hyderabad, India

XTGlobal Infotech Limited · Jun 2022 – Aug 2022

Education

University of Massachusetts Amherst – Isenberg School of Management

M.S. Business Analytics | GPA: 3.8 / 4.0 · Class of 2026

ICFAI Business School

B.B.A., Operations and Supply Chain Management | GPA: 3.46 / 4.0 · Class of 2024

Skills

Learning mindsetProject-based practiceEntry-level technical skillsCommunicationProblem solving

Back to work

Plant Assignment Optimization Using Mixed-Integer Programming | Descriptive & Diagnostic Analytics

Built a Generalized Assignment Problem (mixed-integer linear program) in Python (PuLP/CBC) to optimize plant-order routing across 9,215 real orders and 19 manufacturing plants; found a $146K (0.9%) cost-reduction opportunity while enforcing real capacity, product-eligibility, and contractual (VMI) constraints. Conducted Pareto/ABC concentration, delivery-performance, carrier scorecard, and capacity-utilization analysis using pandas and matplotlib, surfacing operational insights (e.g., 63% of volume concentrated in 5 of 46 customers) beyond the core optimization.

PythonPuLPCBCpandasmatplotlib

View all work

Back to work

Business Analytics Practicum | Strategy & Consulting – MSBDC | UMass Amherst

Performed EDA on MSBDC client, sales, and trade data across 14+ counties (FY22–FY25); mapped international trade activity by country to identify Europe as the dominant export market for MSBDC-assisted businesses, ahead of Canada and Asia-Pacific. Segmented clients by industry and consultation pattern, uncovering a mismatch: MSBDC directed the most consultation hours toward service-based clients, while product-based businesses showed faster export growth in Europe despite receiving comparatively less support. Recommended reallocating consultation resources toward these higher-performing client segments, giving MSBDC leadership data to strengthen funding and policy conversations with state and federal partners on small business investment.

View all work

Back to work

Supply Chain Analytics | Geospatial Optimization – Amazon Prime Air

Designed a 3-hub drone fulfillment network across 14 Massachusetts counties serving 5M+ residents by applying Center of Gravity and Huff Gravity models to demographic, e-commerce, delivery-distance, package-weight, weather, and bandwidth data to site candidate drone hub locations, validating coordinates against Google Earth. Identified Newton hub as capturing 72.35% of statewide demand (4,926 daily deliveries within a 5-mile radius) versus Worcester (1,068) and Springfield (1,020), informing lead-time and inventory-allocation strategy. Recommended Newton as the primary hub, with Worcester as a secondary option, delivering a phased deployment roadmap and executive memo covering procurement trade-offs, rollout-sequencing cost savings, regulatory constraints (FAA BVLOS), and open risks of drone range/endurance and real-time data availability for leadership to resolve before a full state rollout.

Center of Gravity modelHuff Gravity modelGoogle Earth

View all work

Back to work

Pricing Intelligence Model – Airbnb (New Orleans)

Preprocessed 7,444+ Airbnb listings using IQR-based outlier detection; engineered 10+ features including amenity count, host tenure, availability, and guest capacity. Identified bathrooms as the strongest price driver (44% feature importance) via an optimized Random Forest model achieving R² = 0.58 and 86.6% of predictions within $100 of actual price. Uncovered neighborhood-level pricing disparities (French Quarter average: $5,660 vs. citywide median: $111), informing host pricing strategy and platform dynamic-pricing recommendations.

Random ForestIQRfeature engineering

View all work

Back to work

Machine Learning | Model Benchmarking – Credit Card Default Prediction

Benchmarked Logistic Regression (AUC 0.772), Classification Tree (AUC 0.760), and Neural Network (AUC 0.696) across 25+ architectures on 10,000 customer records for credit-default prediction. Performed EDA, data cleaning, dummy-variable creation, tree pruning, and lift-chart analysis to optimize model cutoff thresholds (0.26 selected for best F1 = 0.542). Recommended Logistic Regression for production deployment based on superior F1 score and regulatory interpretability, with a three-tier intervention strategy (high / medium / low risk).

Logistic RegressionClassification TreeNeural Network

View all work

Back to work

Statistical Analysis – Hybrid Work Adoption Among U.S. Employers

Tested the hypothesis that >50% of U.S. companies have permanently adopted hybrid work by 2026, using a systematically constructed sample of 29 companies across industries (IT, Finance, Retail, Manufacturing, Health Services, Hospitality, Government, Education). Observed that the hybrid rate of 62.1% did not reach statistical significance at α = 0.05 (z = 1.30, p = 0.097); 95% Wilson CI [0.44, 0.77] spanned the null boundary. Key takeaway being conclusion is industry conditional, IT industry reached almost 80% hybrid while the other industries were significantly lower.

View all work