Job
What is the Opportunity?
The Data Scientist, Client Risk Rating (AML) is a unique opportunity for a skilled data scientist to join a team focused on developing and enhancing data-driven models that assess client risk scores as part of the Anti-Money Laundering (AML) program. This role is responsible for applying statistical methods and machine learning techniques to analyze customer risk factors, support the design, implementation, and monitoring of risk assessment models, and ensure regulatory compliance.
By working with structured financial data and risk indicators, the Data Scientist will play a critical role in helping detect and prevent financial crimes. This position requires a strong foundation in data analysis, feature engineering, and machine learning techniques, as well as excellent communication skills to collaborate with compliance teams and explain analytical findings.
What Will You Do?
As a Data Scientist, Client Risk Rating (AML), you will be responsible for the following key tasks:
- Support the development of client risk rating models: Apply statistical methods and machine learning techniques to analyze customer risk factors and support the design, implementation, and monitoring of risk assessment models.
- Perform data extraction, cleansing, and transformation: Use SQL and Python to prepare datasets for modeling and ensure data quality and integrity.
- Implement basic machine learning models: Use techniques such as logistic regression, decision trees, and random forests to improve risk classification and model performance.
- Monitor model performance and fine-tune models: Assist in adjusting risk factors and thresholds to improve model accuracy and efficiency.
- Work with large-scale structured datasets: Identify key risk indicators and integrate client attributes, geographic risks, and transaction behaviors into risk models.
- Collaborate with compliance and regulatory teams: Ensure models align with AML/KYC policies and reporting requirements, and provide regular updates on model performance and results.
- Create reports and dashboards: Use data visualization tools to present risk trends and model results to business stakeholders.
- Document model assumptions, limitations, and validation processes: Support regulatory audits and governance requirements by maintaining accurate and transparent documentation of model development and validation.
What Do You Need to Succeed?
To excel in this role, you will need to possess the following skills and experience:
Must-Have:
- 2+ years of experience in data analysis, machine learning, or risk modeling: Proven experience in a financial or compliance-related environment is essential.
- Proficiency in Python, R, or SQL: Strong programming skills in at least one of these languages is required for data analysis and model building.
- Experience with basic machine learning models: Understanding of model evaluation techniques and ability to implement logistic regression, decision trees, and random forests is necessary.
- Ability to work with large datasets: Experience with feature engineering and data visualization is essential for predictive modeling.
- Strong data visualization skills: Ability to present findings effectively using tools such as Tableau, Power BI, Matplotlib, or Seaborn.
- Good communication skills: Ability to collaborate with compliance teams and explain analytical findings is critical.
Nice-to-Have:
- Experience in AML, financial crime compliance, or risk management: Familiarity with regulatory requirements and industry standards is beneficial.
- Familiarity with regulatory requirements: Understanding of KYC/AML risk assessment and compliance requirements is desirable.
- Exposure to cloud-based ML tools: Experience with AWS, Azure, or GCP is a plus.
- Certification such as Certified Anti-Money Laundering Specialist (CAMS): Holding a relevant certification demonstrates expertise and commitment to the field.
Why This Role?
This Data Scientist, Client Risk Rating (AML) role offers a unique opportunity to:
- Gain hands-on experience in AML risk modeling: Work with real-world financial data and develop meaningful solutions to detect and prevent financial crimes.
- Apply machine learning techniques to a critical compliance area: Use data science fundamentals to improve risk classification and model performance.
- Collaborate with experienced data scientists and AML professionals: Work in a structured learning environment to enhance technical skills in data science and risk analytics.
- Develop a strong foundation in data science and risk analytics: Apply machine learning techniques to a critical compliance area and develop a strong understanding of data science fundamentals.
This PL08-friendly version of the job keeps the role practical and hands-on, focusing on data science fundamentals, SQL/Python, and basic ML models rather than advanced AI techniques. If you're a skilled data scientist looking for a challenging and rewarding role, this opportunity is perfect for you.
Frequently Asked Questions
We've compiled a list of frequently asked questions to help you better understand the Data Scientist, Client Risk Rating (AML) role and what it entails.
Q: What is the primary responsibility of a Data Scientist, Client Risk Rating (AML)?
A: The primary responsibility of a Data Scientist, Client Risk Rating (AML) is to develop and enhance data-driven models that assess client risk scores as part of the Anti-Money Laundering (AML) program.
Q: What skills and experience are required for this role?
A: To excel in this role, you will need to possess the following skills and experience:
- 2+ years of experience in data analysis, machine learning, or risk modeling
- Proficiency in Python, R, or SQL
- Experience with basic machine learning models
- Ability to work with large datasets
- Strong data visualization skills
- Good communication skills
Q: What is the difference between a Data Scientist, Client Risk Rating (AML) and a traditional data scientist?
A: A Data Scientist, Client Risk Rating (AML) focuses on developing and enhancing data-driven models that assess client risk scores as part of the AML program. In contrast, a traditional data scientist may work on a wide range of projects, including predictive modeling, data visualization, and data mining.
Q: What kind of data will I be working with?
A: You will be working with structured financial data and risk indicators, including customer profiles, transaction histories, and geographic risks.
Q: Will I have the opportunity to work with cloud-based ML tools?
A: Yes, experience with cloud-based ML tools such as AWS, Azure, or GCP is a plus, but not required.
Q: What kind of certification is preferred for this role?
A: Holding a relevant certification such as Certified Anti-Money Laundering Specialist (CAMS) demonstrates expertise and commitment to the field.
Q: What is the work environment like?
A: You will be working in a structured learning environment with experienced data scientists and AML professionals. The role is hands-on, and you will have the opportunity to develop meaningful solutions to detect and prevent financial crimes.
Q: What are the benefits of working in this role?
A: The benefits of working in this role include:
- Hands-on experience in AML risk modeling
- Opportunity to apply machine learning techniques to a critical compliance area
- Collaboration with experienced data scientists and AML professionals
- Development of a strong foundation in data science and risk analytics
Q: What is the next step in the application process?
A: If you are interested in applying for this role, please submit your resume and cover letter to [insert contact information]. We look forward to reviewing your application.
Q: What is the timeline for filling this position?
A: We are looking to fill this position as soon as possible. The timeline for filling this position will depend on the quality of applicants and the speed of the hiring process.
Q: What is the salary range for this role?
A: The salary range for this role is [insert salary range]. Please note that salary may vary based on location, experience, and other factors.
Q: What kind of support will I receive in this role?
A: You will receive support from experienced data scientists and AML professionals, as well as access to training and development opportunities to enhance your skills and knowledge.
Q: What is the expected outcome of this role?
A: The expected outcome of this role is to develop and enhance data-driven models that assess client risk scores as part of the AML program, and to provide regular updates on model performance and results to business stakeholders.
Q: What is the impact of this role on the organization?
A: This role will have a significant impact on the organization by improving the accuracy and efficiency of client risk classification, and by ensuring regulatory compliance.
Q: What is the future outlook for this role?
A: The future outlook for this role is bright, with the increasing demand for data scientists and AML professionals in the financial industry.