Senior Data Scientist, Vice President

Are you looking for a career move that will put you at the heart of a global financial institution? We are seeking an experienced and exceptionally skilled Senior Data Scientist. This is a highly hands-on individual contributor role that offers the opportunity to directly develop and implement advanced Machine Learning solutions, spearheading innovation within the Credit Risk Technology team and significantly shaping the future of our data and AI product landscape. You will demonstrate a profound passion for pioneering impactful AI applications, taking full ownership from conceptualization through to operational deployment and continuous enhancement. A steadfast commitment to the principles of responsible AI development and stringent model governance within a highly regulated financial context is paramount.

By Joining Citi, you will become part of a global organisation whose mission is to serve as a trusted partner to our clients by responsibly providing financial services that enable growth and economic progress., As a Senior Data Scientist, you will operate as a key individual contributor, driving the end-to-end lifecycle of Data and AI products within a dynamic financial context. Your responsibilities will include:

• Advanced Data Analysis & Modeling: Uncover hidden patterns and develop insights relevant to Credit Risk using advanced statistical, quantitative, and Machine Learning methods. Design and implement robust, scalable machine learning models.
• Model Optimization & Monitoring: Optimize model performance and design automated solutions for model monitoring, including data drift detection and correction strategies.
• Explainable AI for Risk: Apply advanced analytics and AI/ML methods to explain root causes for changes in pre-settlement risk, ensuring transparency and interpretability of models.
• Framework Development: Develop scalable data science frameworks to enable efficient, build-once, deploy-many capabilities for various financial models.
• Production Integration: Develop and integrate machine learning algorithms into production-grade applications, particularly using FastAPI, ensuring robust, performant, and maintainable solutions.
• Strategic Product Contribution & Collaboration: Contribute to the full product lifecycle from ideation and requirements gathering to development, launch, and post-launch performance monitoring, partnering with senior leadership, business analysts and engineering teams to gather requirements, manage expectations, and secure alignment.
• Responsible AI & Governance: Ensure all data products and analytical solutions adhere to Citi's stringent risk, compliance, and regulatory standards, including data privacy and ethical AI principles within the financial domain., This role provides a unique opportunity to significantly contribute to our organization's strategic direction through innovative AI product development within a dynamic financial environment. You will gain in-depth knowledge of advanced financial services operations, working on critical deliverables integral to cutting-edge AI projects. This will enable you to develop valuable new skills, contribute to an advanced AI ecosystem, and accelerate your career in a leading global financial institution.

Do you have a Master's degree?, * Hands-on experience in building and deploying Machine Learning solutions in a production environment.

• Profound knowledge of quantitative methods, probability theory, statistics, and numerical methods.
• Demonstrated MLOps expertise, encompassing model deployment, versioning, lifecycle management, and proficiency in data drift monitoring and correction.
• Proven experience in a quantitative field. Bonus points for exposure to the Financial or Credit Risk industry.
• Exceptional communication, presentation, and interpersonal skills, with a proven ability to influence diverse stakeholders.
• Master's degree in Data Science, Computer Science, or a related quantitative field.
• Proficiency in Python and relevant Machine Learning libraries (e.g. scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM, Pandas, NumPy).
• Experience with API development, specifically FastAPI, for integrating machine learning models into production systems.
• Familiarity with MLOps and orchestration tools like MLflow and Apache Airflow.

Pulled from the full job description

• Employee discount

• Employee assistance programme

• Company pension

• Private medical insurance, By joining Citi London, you will not only be part of a business casual workplace with a hybrid working model (up to 2 days working at home per week), but also receive a competitive base salary (which is annually reviewed), and enjoy a whole host of additional benefits such as:

• Generous holiday allowance starting at 27 days plus bank holidays; increasing with tenure

• A discretional annual performance related bonus

• Private medical insurance packages to suit your personal circumstances

• Employee Assistance Program

• Pension Plan

• Paid Parental Leave

• Special discounts for employees, family, and friends

• Access to an array of learning and development resources

Alongside these benefits Citi is committed to ensuring our workplace is where everyone feels comfortable coming to work as their whole self every day. We want the best talent around the world to be energized to join us, motivated to stay, and empowered to thrive.

Working at Citi is far more than just a job. A career with us means joining a team of more than 230,000 dedicated people from around the globe. At Citi, you'll have the opportunity to grow your career, give back to your community and make a real impact., The Credit Risk Technology team is at the forefront of leveraging data and artificial intelligence to transform critical risk functions within Citi. We develop and maintain complex software solutions that empower Citi's Risk organization to manage counterparty credit risk. Our systems integrate vast datasets, including trades, collateral, market data, and counterparty information to compute essential metrics such as Pre-Settlement Risk (PSE), Exposure at Default (EAD), and Risk Weighted Assets (RWA), alongside optimizing margin allocation, collateral haircuts, liquidity, and concentration levels.