Data Analyst & Optimization Specialist

Oyun Adilbish

Data Analyst ****.*****@*****.*** Linkedin San Francisco, CA

Data-driven analytics professional with extensive experience in data modeling, cost optimization, and large-scale data analysis across 40 countries. Proven ability to analyze complex systems, isolate economic cost drivers, and support strategic corporate decision-making under uncertainty. Expert in Advanced Excel, SQL, and Python, with hands-on experience of forecasting simulations, and cross-functio nal business reporting.

SKILLS

• Technical: Inventory Optimization, Python (Pytorch, Pandas, Numpy, Scikit-learn, Seaborn, TensorFlow), R, SQL, Tablea u, Power BI, Advanced Excel, Gurobi, STATA, Databricks

• Data & Visualization: Data Analysis, Business analytics, Data visualization, AI/ML, Deep Learning

• Modeling & Analytics: Econometrics, Risk management, statistical skill (forecasting, simulations, A/B Testing), Predictive Modeling, Statistical analysis, Machine learning (Random Forest Classifier, Ensemble, KNN, XGBoost), Neural network, CNN, Data manipulation, Quantitative analysis, Probabilistic approach, time-series analysis EDUCATION

University of California, Berkeley – Industrial Engineering and Operations Research May 2026 Master of Science, Analytics

•Coursework: Neural Networks, Reinforcement Learning, Machine Learning, Optimization, Database, Probability and Risk Modeling, Supply Chain Inventory Management

Bay Atlantic University Washington, DC

Bachelor of Arts, Economics and Finance Summa Cum Laude FULL-TIME EXPERIENCE

Data Scientist Intern KONE San Francisco, CA May 2026 – Current

• Engineered a unified, branch-level data architecture in Databricks using SQL and Python to extract and integrate 10+ disparate tables across Salesforce and financial systems

• Building scalable data aggregations and partnered with cross-functional stakeholders to establish a reliable data infrastructure

Research Analyst International Monetary Fund Washington, DC Jul 2023 – Jul 2025

• Collaborated with cross-functional teams of economists to support the analysis of 3 Regional Economic Outlook publications presented to 10,000+ attendees at IMF Annual Conferences

• Analyzed large-scale economic datasets across 40+ countries; visualized financial trend frameworks and policy-driven revenue risks

• Developed scalable reporting frameworks using advanced Excel-based data models (INDEX-MATCH, Pivot Tables) to manage and analyze high-volume datasets for a 2025 IMF Spring Conference Board Presentation

• Built automated reporting tools using Python, SQL to streamline data processing and reduce manual analysis time

• Applied statistical and trend analysis to evaluate economic indicators, supporting data-driven decision-making under uncertainty

IT Consultant Experis US Washington, DC Jul 2022 – Jun 2023

• Translated technical implementations and analytical results into clear recommendations for non-technical stakeholders.

• Optimized sales operations by building automated performance dashboards (Power BI, SQL) for 200+ cross-functional users, providing the business-critical insights needed to track customer engagement.

• Translated technical insights into actionable recommendations for non-technical business stakeholders, improving cros s-functional alignment

Data Scientist Intern Differential Capital Washington, DC Jul 2021 – Sep 2021

• Cleaned and prepared datasets for analysis in Python and reported in Tableau to identify financial market patterns and s upport strategic decision-making. Communicated analytical results through visualizations to stakeholders.

• Drove product vision by building forecasting models and reporting frameworks that identified financial market patterns, improving prediction accuracy by 20% to support strategic decision-making. PROJECTS

Supply Chain Optimization & Analytics Academic Project Berkeley, CA 2025 –2026

• Conducted a comprehensive analysis of postponement strategies in industrial supply chains, identifying opportunities to reduce inventory holding costs and improve risk pooling across maintenance operations

•Simulated inventory policies using (r, Q) models and Little’s Law, optimizing reorder points and service levels under stoc hastic demand conditions

•Managed end-to-end operations in a supply chain simulation, achieving a final balance of $2.68M by optimizing ordering decisions, holding costs, and throughput efficiency Database Engineer E-Commerce Database Design Berkeley, CA 2025 –2026

• Implemented a relational OLTP schema in Google Big Query SQL for a multi-merchant e-commerce platform

• Deployed a weekly Python ETL process to extract transactional data into a star-schema OLAP warehouse, leveraging co nformed dimensions to enable consistent cross-functional analysis. Modeled analytical tables and used Databricks. Data Scientist Google Hackaton Berkeley, CA Oct 2025 – Dec 2025

• Drove business strategy by analyzing large data sets in Python to uncover churn patterns rooted in overpromising during sales and under delivery at onboarding, mapping root causes across the full customer lifecycle.

• Translated findings into a retention roadmap with process improvement recommendations, including an AI agent to prep sales agents on exact product specifications. Awarded 3rd place out of 20+ teams. Machine Learning Engineer Spam Detection Berkeley, CA Aug 2025 – Dec 2025

• Engineered an automated risk-screening system to identify malicious Python packages on PyPI, achieving 100% test reca ll and AUC by training and comparing multiple ML architectures including Random Forest, Gradient Boosting, SVM.

• Developed a high-fidelity risk-scoring framework using Random Forest to prioritize signals from package metadata (e.g., download velocity, release history, naming patterns), reducing manual security audits.

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