Compliance Data Scientist

Barclays•New York, NY

7d•$140,000 - $155,000•Onsite

About The Position

The purpose of the role is to use innovative data analytics and machine learning techniques to extract valuable insights from the bank's data reserves, leveraging these insights to inform strategic decision-making, improve operational efficiency, and drive innovation across the organisation. This role involves identifying, collecting, and extracting data from various sources, performing data cleaning and transformation, developing data pipelines, and designing and conducting statistical and machine learning models. The Compliance Data Scientist will also develop and implement predictive models, collaborate with business stakeholders to add value through Data Science, and advise and influence decision-making. For leadership roles, this includes setting objectives, coaching employees, and demonstrating leadership behaviors. For individual contributors, it involves leading collaborative assignments, guiding team members, identifying new directions for projects, and consulting on complex issues. The role also requires taking ownership for managing risk and strengthening controls, performing work that requires understanding inter-departmental coordination, and collaborating with other areas for business-aligned support. Complex analysis of data from multiple sources will be required to solve problems creatively and effectively, and complex information will need to be communicated and stakeholders influenced to achieve outcomes. All colleagues are expected to demonstrate Barclays Values and Mindset.

Requirements

Mathematics and statistics knowledge
Data analytics and machine learning techniques
Data cleaning, wrangling, and transformation skills
Data pipeline development and maintenance
Statistical and machine learning model design and execution
Predictive modeling
Collaboration with business stakeholders
Leadership skills (for leadership roles)
Coaching and performance appraisal skills (for leadership roles)
Problem-solving skills
Risk management and control strengthening
Understanding of inter-departmental coordination
Complex data analysis from multiple sources
Communication of complex information
Stakeholder influence and persuasion
Knowledge of Barclays Values (Respect, Integrity, Service, Excellence, Stewardship)
Knowledge of Barclays Mindset (Empower, Challenge, Drive)
Experience in identifying, measuring, and preventing Conduct Risk
Experience with Financial Crime Anti-Money Laundering (AML) solutions
Transaction Monitoring system optimization
Non-linear optimization
Development of bespoke reports and dashboards
Identification of outlier customer and client behavior
Generation of complex insights and actionable Management Information (MI)
Barclays automated dashboard development tools
Enhancement of AML group effectiveness and efficiency
Partnership with Barclays Technology teams
Enhancement of data quality and structure for Conduct Risk analytics
Development of innovative data insight methods using graph databases and Network Link Analytics
Stakeholder engagement to identify problems and needs
Keeping up-to-date with external industry developments
Research and peer-group meetings and conferences
Data Visualization tools for performance monitoring
Data analytics for root cause analysis, diagnostics, and simulations
Production of performance metrics for Market Surveillance processes
Identification of Market Abuse risk
Spotting trends and patterns indicating broader Conduct Risk

Nice To Haves

Experience with graph databases
Experience with Network Link Analytics approaches

Responsibilities

Identification, collection, extraction of data from various sources, including internal and external sources.
Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
Development and maintenance of efficient data pipelines for automated data acquisition and processing.
Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data.
Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.
Advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness.
Collaborate closely with other functions/ business divisions.
Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function.
Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes.
Lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments.
Identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
Take ownership for managing risk and strengthening controls in relation to the work done.
Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy.
Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively.
Communicate complex information.
Influence or convince stakeholders to achieve outcomes.
Utilize mathematics and statistics to perform data analysis to support the identification, measurement, and prevention of Conduct Risk.
Provide solutions for Financial Crime Anti-Money Laundering (AML) ranging from Transaction Monitoring system optimization through non-linear optimization to the development of bespoke reports and dashboards to identify outlier customer and client behavior.
Generate complex insights and actionable Management Information (MI) using Barclays automated dashboard development tools.
Enhance the effectiveness and efficiency of the Company’s AML group.
Partner with Barclays Technology teams to enhance the quality and structure of data used for subsequent Conduct Risk analytics.
Develop innovative ways to gain additional insight from existing data by using graph databases and similar Network Link Analytics approaches.
Engage with stakeholders to identify their key problems and needs, keeping up-to-date with external industry development through research and peer-group meetings and conferences.
Use Data Visualization tools to produce performance-monitoring metrics for Transaction Monitoring processes.
Use data analytics to inform root cause analysis, diagnostics, and to conduct simulations.
Produce performance metrics for Market Surveillance processes designed to identify Market Abuse risk.
Use data analytics to spot trends and patterns in data that could indicate broader Conduct Risk, including Conduct Risks relating to Financial Crime and/or Market Abuse.