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;

• Implemented income estimation, loan limit estimation, and probability of default models to replace manual decision-making processes with automated ML frameworks for a public bank in Turkey;
• Designed and developed CRM segmentation and propensity-to-buy models for a private bank using customer data to enhance the effectiveness of marketing campaigns;
• Conducted data preprocessing, feature engineering, and model selection to optimize model performance and accuracy;
• Oversaw the deployment of machine learning models into production, ensuring seamless integration with existing systems and processes;
• Stayed updated on the latest developments in machine learning and data science to ensure the application of best practices and state-of-the-art techniques.

• 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.

• Implemented NLP and clustering models to effectively segment Unilever's digital consumer landscape based on online behavior data, enhancing the company's understanding of its customer base;
• Designed and oversaw the rollout of a Power BI dashboard platform, which aggregated real-time marketing and social analytics data, making it a valuable tool for decision-makers within the organization;
• Achieved recognition by having the Power BI dashboard platform included in Unilever's Top 20 most active dashboards globally, reflecting its high impact and usage across the company;
• Strong analytical and technical skills in utilizing NLP and clustering techniques to extract meaningful patterns from large datasets, driving data-driven decision-making at Unilever;
• Contributed significantly to Unilever's market understanding and marketing effectiveness by providing data-driven insights and user-friendly dashboards that aided strategic planning and execution.

• Played a pivotal role in designing the data collection frameworks for Unilever's inaugural B2B (Business-to-Business) and D2C (Direct-to-Consumer) brands;
• Demonstrated a solid ability to strategize and implement data collection processes that aligned with Unilever's goals and objectives, contributing to the company's data-driven decision-making capabilities;
• Maintained a keen focus on data privacy and compliance, ensuring that all data collection activities adhered to relevant regulations and industry standards;
• Continuously monitored and refined the data collection frameworks to adapt to changing market dynamics and consumer behaviors, enhancing Unilever's ability to stay agile and responsive in the marketplace;
• Ultimately, it was pivotal in laying the foundation for data-driven insights and personalized customer experiences for Unilever's B2B and D2C brands, contributing to their growth and success.

• 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.