Data Scientist

Data Scientist

Location:

İstanbul / Ankara

Work Model:
Hybrid

We are supporting one of our client companies in their search for a Data Scientist.

The selected candidate will contribute to the development of the company’s next-generation AI and quantitative analytics infrastructure. You will work with large-scale financial, operational, and alternative datasets to build, evaluate, and optimize predictive models that enhance forecasting, trading analytics, and risk management.

This role focuses on transforming complex, multi-source data into predictive systems that strengthen the company’s analytical and trading performance.

• Collect, preprocess, and structure large-scale financial, operational, and alternative datasets to support quantitative and AI modeling.
• Develop and test mathematical, statistical, and econometric models to identify patterns, anomalies, and predictive relationships in target markets.
• Conduct time-series analysis, feature engineering, and hypothesis testing to extract predictive insights.
• Support model training, validation, and versioning pipelines to ensure reproducibility and scalability.
• Collaborate with quantitative analysts and engineers to integrate data-driven models into production environments.
• Contribute to the continuous improvement of data quality, reliability, and performance metrics.

• Bachelor’s or Master’s degree in Mathematics, Applied Mathematics, Statistics, Data Science, Computer Science, or a closely related discipline.
• Solid understanding of calculus, linear algebra, probability, statistics, optimization, numerical methods, and algorithmic reasoning.
• Minimum 2 years of experience in applied mathematical modeling or data analysis within scientific, engineering, or financial domains.
• Proficiency in Python (pandas, Num Py, scikit-learn, matplotlib) for data manipulation and model development.
• Experience with time-series data, regression models, and statistical hypothesis testing.
• Ability to build and manage data workflows in big data and cloud environments (e.g., Spark, AWS, GCP).

• Experience with machine learning or deep learning for financial data applications.
• Experience with financial data APIs (Bloomberg, Refinitiv, yFinance, etc.).
• Relevant certifications such as CFA, FRM, or training in Quantitative Finance.

• Ability to communicate complex quantitative and financial concepts clearly to non-technical stakeholders.
• Strong collaboration and coordination capabilities across cross-functional teams.
• Proactive, solution-oriented, and adaptable in fast-changing market environments.