Oguz K.

• Designed and deployed an ML-based record linkage system for hotel booking data, using probabilistic matching algorithms to improve customer data completeness by 12%, enabling better tracking of customer behavior and spending patterns;

• Implemented an automated booking prediction framework covering 400+ hotels and 25M+ customers, leading to a 17% increase in conversion rates by replacing manual decision processes;

• Built and maintained scalable data pipelines integrating multiple data sources with automated weekly predictions and monthly retraining schedules;

• Architected end-to-end MLOps solutions on Kubernetes, ensuring reliable model deployment and performance monitoring;

• Led the development of containerized APIs and automated CI/CD pipelines, streamlining the deployment process and improving system reliability;

• Collaborated with product and business teams to align technical solutions with business objectives and measure impact;

• Integrated external data sources and implemented dynamic feature selection to enhance model performance and adaptability;

• Developed comprehensive monitoring systems to track model performance and data quality in production;

• Optimized model training and inference pipelines to handle large-scale data processing efficiently;

• Implementerede modeller til estimering af indkomst, lånegrænse og sandsynlighed for misligholdelse med henblik på at erstatte manuelle beslutningsprocesser med automatiserede maskinlæringsrammer for en offentlig bank i Tyrkiet;
• Designede og udviklede CRM-segmentering og propensity-to-buy-modeller for en privat bank ved hjælp af kundedata for at forbedre effektiviteten af marketingkampagner;
• Udførte dataforbehandling, funktionsteknik og modelvalg for at optimere modellens ydeevne og nøjagtighed;
  • Monitorerede implementeringen af maskinlæringsmodeller i produktionen og sikrede nem integration med eksisterende systemer og processer;
  • Holdt sig opdateret om den seneste udvikling inden for maskinlæring og datavidenskab for at sikre anvendelsen af bedste praksis og avancerede teknikker.

• Provided data science and analytics services for ad-hoc projects, demonstrating a strong commitment to delivering data-driven insights and solutions;

• Developed a multiclass clustering pipeline by utilizing embedding techniques, specifically Bio ClinicalBERT, combined with neural network models. This pipeline was employed to predict patient ICD codes based on hospital reports for a medical startup company;

• Successfully implemented campaign digital performance prediction models for an e-commerce company, enabling data-driven decision-making in their marketing efforts;

• Employed anomaly detection models to identify and highlight unusual patterns or outliers in the digital campaign performance data, helping the e-commerce company proactively address issues and optimize their strategies;

• Collaborated closely with project stakeholders to gather requirements, understand project objectives, and define success criteria;

• Conducted data preprocessing, feature engineering, and model development, ensuring the robustness and accuracy of the solutions provided;

• Leveraged state-of-the-art data science and machine learning techniques to deliver high-quality and actionable insights to clients;

• Maintained effective communication with project teams, providing regular updates on project progress and addressing any questions or concerns;

• Demonstrated adaptability and a problem-solving mindset when faced with complex and challenging data science tasks;

• Contributed to the success of medical startup and e-commerce companies by delivering data-driven solutions that improved their decision-making processes and overall performance.

• Implementerede NLP- og clustering-modeller til effektivt at segmentere Unilevers digitale forbrugerlandskab baseret på online adfærdsdata, hvilket forbedrede virksomhedens forståelse af sin kundebase;
• Designede og førte tilsyn med udrulningen af en Power BI-kontrolpanelplatform, som aggregerede marketing- og socialanalysedata i realtid, hvilket gjorde det til et værdifuldt værktøj for beslutningstagere i organisationen;
• Opnåede anerkendelse ved at få Power BI-kontrolpanelplatformen inkluderet i Unilevers Top 20 mest aktive kontrolpaneler på globalt plan, hvilket afspejler dens store indflydelse og brug på tværs af virksomheden;
• Solide analytiske og tekniske færdigheder i forbindelse med at bruge NLP- og clustering-teknikker til at udtrække meningsfulde mønstre fra store datasæt, hvilket var grundlaget for datadrevet beslutningstagning hos Unilever;
• Bidrog væsentligt til Unilevers markedsforståelse og markedsføringseffektivitet ved at levere datadrevet indsigt og brugervenlige kontrolpaneler, der bidrog til strategisk planlægning og udførelse.

• Spillede en afgørende rolle i udformningen af dataindsamlingsrammerne for Unilevers første B2B- (Business-to-Business) og D2C-brands (Direct-to-Consumer);
• Demonstrede færdigheder i at lægge strategier for og implementere dataindsamlingsprocesser i overensstemmelse med Unilevers målsætninger, hvilket bidrog til virksomhedens datadrevne beslutningstagningskapacitet;
• Havde et skarpt fokus på databeskyttelse og compliance og sikrede, at alle dataindsamlingsaktiviteter overholdt relevante regler og branchestandarder;
• Overvågede og forbedrede løbende rammerne for dataindsamling for at tilpasse sig skiftende markedsdynamik og forbrugeradfærd, hvilket forbedrede Unilevers evne til at forblive agil og lydhør på markedet;
• I sidste ende var det afgørende at lægge grundlaget for datadrevet indsigt og personaliserede kundeoplevelser for Unilevers B2B- og D2C-brands, hvilket bidrog til deres vækst og succes.

• Assumes responsibility for overseeing all Google Analytics implementations across brand and campaign websites, ensuring accurate tracking and data collection;

• Manages the setup and configuration of Google Analytics for multiple websites, ensuring that the tracking code is correctly deployed and tracking goals are defined;

• Collaborates with stakeholders to define and document key performance indicators (KPIs) for digital performance measurement, aligning reporting with business objectives;

• Regularly monitors and analyzes website traffic and user behavior using Google Analytics to provide actionable insights for optimizing digital strategies;

• Generates comprehensive reports and dashboards to track digital KPIs, presenting findings to relevant teams and stakeholders;

• Conducts data quality checks and troubleshooting to address any issues or discrepancies in Google Analytics data;

• Stays up-to-date with industry best practices and Google Analytics updates to continuously improve tracking and reporting processes;

• Ensures data privacy and compliance by implementing appropriate settings and configurations within Google Analytics;

• Works collaboratively with web development and marketing teams to implement tracking enhancements and improve data accuracy;

• Provides guidance and training to team members on Google Analytics and digital performance reporting to enhance the team's analytical capabilities.