Research And Development Engineer 'Applied Data Science'

About:

Yapı Kredi Technology is a technology company that produces innovative, high quality, and high value-added products and solutions in the finance sector. With more than 1,700 employees, it aims to create products that will shape the sector for Yapı Kredi Bank and to be the undisputed leader in the field of technology by using modern architectural systems and cloud technologies. It also contributes to the development of new and exemplary products for the sector by using natural language processing, machine learning, artificial intelligence, and data mining technologies with its R&D team.

Who We Are:

At Yapı Kredi Technology, we research with passion, wonder as we learn, and implement innovations that shape the future together. We take responsibility from the first day with our expert colleagues and work with all our strength for pioneering applications. We make quick decisions and take action. We quickly adapt to innovations and changes.

What Do We Offer:

📌 Opportunity to work in hybrid model

💻Opportunity to work in Koç Group Community Companies' offices

🌴Chance to discover the natural wonders and amenities offered at Koç Toplululuğu Spor Kulübü (KTSK)

🚀Career development opportunities in a structured technology career path

🎓 Unlimited access to more than 10,000 content on the Percipio by Skillsoft technical development platform

💫 Opportunity to benefit from BizClub and KoçAilem privileges exclusive to Yapı Kredi Technology employees

🤝Company-contributed individual retirement insurance

🎂 Birthday off day

We are seeking talented R&D Engineers with a passion for solving real-world problems using machine learning and data analysis techniques, particularly in fraud and anomaly detection, handling imbalanced tabular datasets, and time series forecasting, to join our Applied Data Science team.

You will be working closely with other fast-paced and highly-skilled R&D engineers in a pro-active and R&D oriented software development team to take ML models from R&D to production, scale and maintain them. As a full-stack R&D Engineer, you will take a role in each phase of the project, i.e. data analysis, technical design, prototyping, ML development, integration development to core banking systems and end-to-end testing.

Qualifications:

• Having / Pursuing or willing to have an M.Sc. or Ph. D. degree in Machine Learning/Deep Learning related field from Computer, Industrial or Electrical and Electronics Engineering,
• Demonstrated expertise in coding and problem-solving using Python,
• Extensive experience and fluency in utilizing data science tools such as pandas, numpy, matplotlib, scipy, and scikit-learn for data manipulation, analysis, and visualization,
• A keen interest and enthusiasm for tackling the complexities of imbalanced data and developing effective solutions,
• Familiarity with advanced machine learning and deep learning techniques, including sequence modeling (e.g., for time series analysis) and ensemble methods (e.g., gradient boosting algortihms), to solve complex problems in various domains.
• Practical experience applying advanced machine learning techniques to real-world datasets, with a demonstrated ability to analyze results, fine-tune models, and troubleshoot issues.
• Strong understanding of ML training, testing, data processing steps and ML project pipelines,
• Effectively communicate technical concepts and project updates to the stakeholders, ensuring alignment and understanding,
• Eager to follow technical literature and willing to contribute actively by publishing academic papers in international conferences.

Would be Advantageous:

• Experience with version control systems (Git, Git LFS, …)
• Experience with object-oriented programming languages (Java, C#, C++, …)
• Familiarity with Linux (Debian, Ubuntu, Fedora, RHEL, …)
• Experience in building and consuming web services (REST, SOAP, gRPC, …)
• Experience with SQL databases (Oracle, PostgreSQL, SQL Server, …)
• Experience with NoSQL databases (MongoDB, Redis, Apache Cassandra, …)
• Experience with containerization and orchestration tools (Docker, Podman, Kubernetes, …)
• Experience with cloud computing platforms (AWS, Azure, Google, …)
• Experience with data visualization tools (Power BI, Tableau, …)
• Experience with workflow tools (Apache Airflow, Kubeflow, MLflow, …)

Candidate Selection Process:

Our recruitment process for all positions typically encompasses technical interviews, director assessments, competency evaluations, and personality tests. We will extend our offer to candidates who have successfully completed a positive evaluation process.

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