Senior Data Scientist - Global Finance Leader

Senior Data Scientist - Global Finance Leader

We are seeking a highly skilled Senior Data Scientist to join a global finance firm and tackle some of the most complex challenges in markets, risk, and investment strategy. You will work with large-scale financial, transactional, and alternative datasets, applying advanced statistical methods, machine learning, and AI to generate insights that directly influence trading, risk management, and business decisions across global markets. This role sits at the intersection of data science, engineering, and finance. You will partner with quantitative researchers, traders, and technologists to design models, build scalable data products, and deliver solutions that provide a measurable competitive advantage. You will also contribute to the evolution of firm-wide data science practices, mentoring peers and setting best practices for analytics in high-stakes environments. Responsibilities

Model Development: Build, test, and deploy predictive and statistical models for trading, risk, and investment strategies. Experimentation: Design and analyze experiments to evaluate new data sources, trading hypotheses, or risk frameworks. Advanced Analytics: Apply machine learning, natural language processing, and time-series forecasting to financial and alternative datasets. Data Engineering Collaboration: Partner with data engineers to ensure data pipelines and infrastructure are scalable, reliable, and optimized for research. Business Impact: Translate complex data findings into actionable insights for executives, traders, and global stakeholders. Mentorship: Share knowledge of statistical methodology, ML techniques, and applied research best practices with junior team members. Requirements

Professional Experience: 6+ years of applied data science experience, preferably in finance, technology, or similarly data-rich industries. Technical Skills: Proficiency in Python, R, or Scala, with strong SQL expertise; experience with ML libraries such as scikit-learn, TensorFlow, or PyTorch. Statistical Foundation: Strong knowledge of probability, econometrics, experimental design, and causal inference. Financial Domain Knowledge: Familiarity with markets, risk modeling, or portfolio optimization is highly desirable (but not strictly required). Big Data Tools: Experience working with large-scale distributed systems (Spark, Hadoop, or cloud-native data platforms). Collaboration: Ability to work effectively with quantitative researchers, engineers, and business leaders in a global setting. Education: Master’s or PhD in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. We are an equal opportunities employer and welcome applications from all qualified candidates.

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