About the Role
The role is within the EMEA CDO Data Analytics team, part of the EMEA Chief Data Office (CDO). The team is responsible for managing the UK data quality programme and implementing improved data governance and management practices across the EMEA region. The UK data quality programme focuses on enhancing Citi’s approach to data risk and addressing regulatory commitments. This role, Data Science Lead Analyst, is a strategic professional contributing to directional strategy within their field. The purpose of this role is to perform data analytics and data analysis across different asset classes and to build data science capabilities within the team, working closely with the wider EMEA CDO team to deliver business priorities.
Responsibilities
- Conducts strategic data analysis, identifies insights and implications, makes strategic recommendations, and develops data displays to communicate complex analysis clearly.
- Builds, maintains, and performs Data Analytics on various asset classes such as Commodities, Equities, Rates, and Loans.
- Builds or advises on operational models and bespoke analysis for each use case.
- Builds data science capabilities using Python and other analytical tools.
- Performs complex data analytics (data cleansing, transformation, joins, aggregation etc.) on large complex datasets.
- Builds analytics dashboards using PowerBI/Tableau.
- Produces high-quality supporting business data analysis for asset classes, financial products, systems, and reports in a fast-paced environment.
- Communicates complicated findings and proposes solutions to various stakeholders.
- Understands business and functional requirements provided by business analysts and converts them into technical design documents.
- Works closely with cross-functional teams (e.g., Business Analysis, Product Assurance, Platforms and Infrastructure, Business Office, Control and Production Support). Prepares handover documents and manages SIT, UAT, and Implementation.
- Identifies and proactively performs data analysis to identify and resolve issues that could impact UK/EMEA.
- Demonstrates an in-depth understanding of how the development function integrates within overall business/technology to achieve objectives, requiring a good understanding of the banking industry.
- Designs, Implements, Integrates, and tests new features.
- Explores existing application systems, determines areas of complexity, and potential risks to successful implementation.
- Contributes to continual improvement by suggesting improvements to software architecture, software development processes, and new technologies.
- Performs other duties and functions as assigned.
- Appropriately assesses risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.
Knowledge/Experience
- Professional experience working in Financial services or Finance IT industry is a must.
- Strong knowledge of Python 3.x and rich experience in Pandas, PySpark, Numpy, OS, Sys, RE, XML, JSON, Pyarrow, AWSLibrary.
- Strong knowledge of ETL methodology and knowledge in one or more of Knime, Alteryx, Informatica, Abinitio, AWS Glue.
- Strong Knowledge in one or more of the BI visualisation tools such as Tableau, PowerBI.
- Knowledge and understanding of machine learning libraries like scikit-learn, opencv, tensorflow.
- Very Good knowledge of RDBMS – one or more of the following: Oracle, MySQL.
- Ability to write complex SQL needed to investigate data issues and analyse legacy data.
- Good knowledge in UNIX Shell/Perl/Windows scripting.
- Proven experience in working for complex data warehouses.
- Knowledge of automation tools like Power Automate, Selenium.
Qualifications
- MBA or Advanced Degree in Information Systems, Business Analysis / Computer Science.
- 6-10 years experience using tools for statistical modeling of large data sets.
- Process Improvement or Project Management experience.
Education
- Bachelor’s/University degree or equivalent experience, potentially Master's degree.