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- Tasks: Analyse complex data sets to detect financial crime patterns and collaborate on prevention strategies.
- Company: Capgemini is a global leader in business and technology transformation, committed to sustainability and inclusivity.
- Benefits: Enjoy a supportive community, career empowerment, and opportunities for remote work and professional growth.
- Why this job: Join a dynamic team making a real impact in the fight against financial crime with cutting-edge technology.
- Qualifications: Bachelor's degree in Data Analytics or related field; 3-5 years experience in financial crime data analysis required.
- Other info: Proficiency in SQL, Python, and data visualisation tools like Tableau is essential.
The predicted salary is between 36000 - 60000 £ per year.
Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
The Data Analyst - Financial Crime will be responsible for analyzing complex data sets to identify patterns, trends, and anomalies related to financial crime. This role involves collaborating with cross-functional teams to develop and implement data-driven strategies for detecting and preventing financial fraud and other illicit activities.
- Data Analysis: Analyze transactions, accounts, customer data, and alerts to identify suspicious patterns and potential risks.
- Model Development: Design and implement financial crime detection models and scenarios using statistical and analytical tools.
- Reporting: Generate detailed reports and visualizations to communicate findings to stakeholders and support decision-making processes.
- Root Cause Analysis: Conduct root cause analyses on financial crime incidents to enhance detection and prevention strategies.
- Collaboration: Work closely with investigators, compliance teams, and other departments to translate data-driven insights into actionable recommendations.
- Data Management: Ensure the accuracy, integrity, and security of data used for analysis and reporting.
Your Profile:
- Bachelor's degree in Data Analytics, Statistics, Finance, or a related field.
- Advanced degree preferred.
- Proven experience in the financial crimes/AML space, with a minimum of 3-5 years in data analysis roles.
- Proficiency in data analytics tools such as SQL, Python, R, and SAS.
- Experience with data visualization tools like Tableau.
- Strong understanding of AML/KYC regulations and practices.
- Excellent analytical and problem-solving skills, with the ability to interpret complex data sets.
- Strong communication skills, with the ability to translate complex data into actionable insights.
- Experience working in a major financial institution or consulting firm.
- Certification in financial crime prevention or related areas.
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world while creating tangible impact for enterprises and society.
Data Analyst - Financial Crime employer: Capgemini
Contact Detail:
Capgemini Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analyst - Financial Crime
✨Tip Number 1
Familiarise yourself with the latest trends in financial crime and anti-money laundering (AML) regulations. This knowledge will not only help you understand the role better but also allow you to engage in informed discussions during interviews.
✨Tip Number 2
Brush up on your data analytics skills, particularly in SQL, Python, and R. Consider working on personal projects or contributing to open-source projects that showcase your ability to analyse complex data sets relevant to financial crime.
✨Tip Number 3
Network with professionals in the financial crime sector. Attend industry conferences, webinars, or local meetups to connect with others in the field. This can lead to valuable insights and potential referrals for job openings.
✨Tip Number 4
Prepare to discuss specific examples of how you've used data analysis to solve problems in previous roles. Be ready to explain your thought process and the impact of your findings, as this will demonstrate your analytical skills and problem-solving abilities.
We think you need these skills to ace Data Analyst - Financial Crime
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data analysis, particularly in the financial crime or AML space. Use specific examples that demonstrate your proficiency with tools like SQL, Python, and Tableau.
Craft a Compelling Cover Letter: Write a cover letter that connects your skills and experiences to the job description. Emphasise your analytical abilities and your understanding of AML/KYC regulations, and explain why you are passionate about preventing financial crime.
Showcase Your Technical Skills: In your application, clearly outline your technical skills and experience with data analytics tools. Mention any relevant certifications in financial crime prevention to strengthen your application.
Prepare for Potential Assessments: Be ready for potential assessments or tests that may evaluate your data analysis skills. Brush up on statistical methods and data visualisation techniques that are relevant to the role.
How to prepare for a job interview at Capgemini
✨Showcase Your Analytical Skills
Prepare to discuss specific examples of how you've analysed complex data sets in the past. Be ready to explain your thought process and the tools you used, such as SQL or Python, to derive insights.
✨Understand Financial Crime Regulations
Brush up on AML/KYC regulations and practices before the interview. Demonstrating your knowledge in this area will show that you're not only technically skilled but also aware of the compliance landscape.
✨Prepare for Scenario-Based Questions
Expect questions that ask you to solve hypothetical financial crime scenarios. Practise articulating your approach to identifying suspicious patterns and how you would develop detection models.
✨Communicate Clearly
Since the role involves translating complex data into actionable insights, practise explaining your findings in a clear and concise manner. Use visualisation tools like Tableau to illustrate your points if possible.
