Data Scientist, İstanbul | Koç Kariyerim

• Solve complex business problems by developing impactful data science solutions.
• Lead and participate in all phases of data science projects, including data preparation, modeling, validation, and deployment.
• Clean, transform, and analyze structured and unstructured data to uncover actionable insights.
• Build machine learning models and statistical algorithms for tasks like classification, regression, and clustering.
• Tune hyperparameters and assess model performance using appropriate evaluation metrics.
• Track experiments, version models, and monitor performance using open-source tools.
• Communicate findings and recommendations clearly to both technical and non-technical stakeholders.
• Work closely with business teams to co-develop scalable, business-driven solutions.
• Apply data privacy and ethical practices across the entire project lifecycle.
• Continuously explore new methods, tools, and trends in data science and machine learning.

• Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Industrial Engineering, or a related quantitative field.
• Minimum 3 years of hands-on experience in data science, machine learning, or analytics roles.
• Strong programming skills in Python and SQL, including libraries such as pandas, scikit-learn, matplotlib, LightGBM, and XGBoost.
• Use hyperparameter tuning tools (e.g., Optuna) and apply robust model evaluation metrics (e.g., AUC, F1).
• Manage experiment tracking, model versioning, and basic monitoring with open-source platforms like MLflow.
• Apply Docker to containerize environments and ensure consistency across development, testing, and deployment stages.
• Deploy machine learning models in production systems and integrate them into business workflows or customer-facing applications.
• Demonstrate strong analytical thinking, effective communication, and a proactive, solution-oriented approach.

Preferred Qualifications

• Deploy ML models via REST APIs using frameworks like Flask or FastAPI.
• Design and evaluate experiments using A/B testing and causal inference techniques.
• Automate and schedule data workflows using orchestration tools such as Apache Airflow.
• Collaborate on client-facing or cross-functional projects and translate business needs into technical solutions.
• Understand techniques for model monitoring, data drift detection, and retraining strategies.