Quantitative Analyst

Location: İstanbul/AnkaraAbout InoTrade At InoTrade, we shape the future of global commerce by leading in AI-driven innovation. By combining advanced AI capabilities with deep industry expertise, we transform complexity into clarity and deliver solutions that drive meaningful, lasting impact.What makes this possible is our people. We bring together AI experts, engineers, researchers, and industry leaders who thrive on collaboration and curiosity. United by a passion for solving the toughest challenges, our team creates breakthroughs that push the boundaries of technology and business. RoleThe Quantitative Analyst plays a key role in advancing InoTrade’s AI models. Working with large-scale financial and operational datasets, you will design, test, and optimize predictive frameworks that strengthen forecasting, risk analytics, and quantitative strategies. This role bridges research and execution, turning data-driven insights into actionable market strategies by applying innovative statistical and algorithmic methods. Responsibilities Conduct in-depth statistical analysis and modeling on financial and operational datasets.Ensure model quality, theoretical soundness, and consistency through both qualitative and quantitative assessments.Transform academic and industry research into actionable insights.Develop new models, generate signals, and convert them into applicable strategies.Calibrate strategies across different products and adapt them to changing market conditions.Design and execute backtesting and scenario analyses to assess model behavior under historical market conditions.Collaborate with cross-functional teams to ensure seamless integration of AI models into defined objectives.Required Qualifications MS or Phd degree in Financial Engineering, Data Science, Statistics, Mathematics, Economics, Computer Engineering or a related discipline.Minimum 2 years of experience in quantitative analysis, data science, or related analytical roles.Proficiency in Python, R, or C++ for quantitative modeling and simulation.Strong foundation in time-series analysis and financial data modeling.Excellent analytical reasoning and problem-solving ability.Preferred QualificationsExperience with machine learning or deep learning for financial data applications.Familiarity with big data and cloud environments (e.g., Spark, AWS, GCP).Experience with financial data APIs (Bloomberg, Refinitiv, yFinance, etc.).Relevant certifications such as CFA, FRM, or training in Quantitative Finance.Soft SkillsAbility to communicate complex quantitative and financial concepts clearly to non-technical stakeholders.Strong cross-team collaboration and coordination capabilities.Proactive, solution-oriented, and adaptable in fast-changing market environments.