Emil Aydinsoy

Chemical Process Optimization & Modeling:
Conducted advanced research to identify optimal solutions for chemical and chemical engineering processes, focusing on reaction optimization and system efficiency.

Analytical & AI-Driven Methods:
Applied mathematical optimization techniques, statistical analysis, and machine learning—including AI and deep learning models—to solve complex chemical engineering problems, enhancing predictive accuracy and process outcomes.

Cross-functional Collaboration & Knowledge Sharing:
Delivered presentations and communicated research findings to interdisciplinary teams, contributing to scientific publications and fostering innovation within the research community.

• Applied Data Science & Machine Learning Instruction:
  Deliver engaging lectures and hands-on sessions in Machine Learning, Data Analysis, Deep Learning, and Optimization Models, with a focus on real-world applications and problem-solving.
• Curriculum Development & Student Mentorship:
  Design and continuously improve course content, integrating Python programming, Power BI, and statistical methods to ensure students gain practical, industry-relevant skills. Mentor students on projects ranging from exploratory data analysis to advanced machine learning applications.

• Data Digitalization & Standardization: Spearheaded the digital transformation of data assets, consolidating and structuring disparate data sources—including spatial datasets—into centralized, accessible repositories.
• Geospatial Modeling & Implementation: Developed and optimized GIS models to solve complex spatial challenges, facilitating the integration of location intelligence into core business processes and operational decision-making.
• Data Pipelines & Executive Dashboards: Built robust data pipelines and developed dynamic dashboards (Power BI/Tableau) tailored for upper management, delivering real-time visibility into key metrics and geospatial insights for strategic planning.

• Data Pipeline Management: Maintained and optimized ELT/ETL pipelines on MySQL Server, ensuring data integrity and seamless integration across analytics platforms.
• Codebase Modernization: Migrated and refactored legacy R Studio scripts into scalable, maintainable solutions using modern tools, enhancing compatibility and processing efficiency.
• KPI Dashboards & Operational Insights: Designed and deployed Power BI dashboards to monitor technical support operations and provider performance, driving data-informed decisions for KPI growth.
• Digital Transformation Leadership: Spearheaded the company's data digitalization initiatives, standardizing data processes and promoting a culture of data-driven decision-making across departments.

• ETL Management: Ensured the integrity of ETL pipelines and conducted analytical experiments to solve domain-specific challenges.
• Dashboards & Insights: Developed Tableau dashboards to deliver actionable insights to stakeholders.
• Machine Learning Models: Built classification and regression models to categorise and predict subsidy requirements and potential revenue, enhancing predictive accuracy by 20-25%.
• Deployed the model to AWS ECS. Make a ready-to-use tool for the stakeholders for reporting and analysing purposes.

• Data Integration Pipeline: Designed and implemented a centralized Azure database, integrating data from employee systems, client management, delivery routes, and sales platforms.
• Demand Forecasting: Built a time series model and Streamlit app to predict SKU and store demand, improving procurement accuracy and sales planning by 25-30%. The model was deployed locally in a streamlit app.
• Workforce Optimization: Developed an HRMS optimisation algorithm for automated task scheduling and strategic day-off planning, reducing workforce planning time by 50%.
• Deployment: For the projects, Demand Forecasting and HRMS created a streamlet application for reporting, analysis, and decision-making. An additional data pipeline was created for storing the reports in the server.

• Data Model Expansion: Optimized and expanded data models, enabling 15+ new use cases and ensuring seamless system integration.
• AWS & SQL Expertise: Enhanced data ingestion and processing within the data lake, warehouse, and ML platform.
• Scalable Solutions: Developed cost-efficient AWS QuickSight solutions, boosting performance by 25%.

• Data Pipelines: Developed end-to-end pipelines to streamline FMCG metric processing and analysis.
• KPI Monitoring: Built a custom system to track real-time performance trends and critical KPIs.
• Data Governance: Established regulations and comprehensive data dictionaries, ensuring data accuracy and compliance.

• ELT Pipeline: Designed and implemented a centralized real-time tender database via web scraping and API integration.
• Predictive Model: Developed a model to identify high-priority tenders ('hot call' tenders) and integrated insights into an interactive dashboard for swift sales action. Reduced manual data processing by 40% and streamlined tender tracking, improving business outcomes.
• Deployment: Created an SQL database in Azure and deployed the model predictions to AKS.

• Data Analysis: Analyzed 60+ technical help desk datasets, identifying bottlenecks and reducing response time by 20%.
• Interactive Dashboard: Built a real-time support metrics dashboard, boosting resolution rates by 15% and enabling data-driven decisions.
• Workflow Optimization: Conducted statistical analyses to uncover trends, enhancing service performance and customer satisfaction.

• ERP & CRM Development: Built a fully automated system from scratch, integrating platforms via APIs into a centralized VPS-hosted database.
• Process Automation: Streamlined core processes, including invoicing, dynamic reporting, and recommendation engines.
• Efficiency Gains: Improved operational efficiency by 30-40% and reduced manual workload by 50%.

• Introducing cutting-edge predictive analytics solutions in multiple business areas including finance, logistics, and sales.
• Including collecting past and present data, analyzing collected data, using relevant computer software, finding ways to improve operations and reduce costs, managing a team of data scientists, planning data projects, and providing the tools needed to make strategic decisions.