Location: Hybrid - Toronto or Markham; 3 days in office and 2 days work from home, with potential to move to 4 days in office.
Anchor days: Markham on Monday and Toronto on Wednesday.
Duration: 6 months, starting ASAP; extension and conversion are possible based on business needs and performance.
Schedule: Monday–Friday, core business hours; 37.5 hours per week; 7.5 hours per day.
Job Description
- Our client is seeking a senior AML Data Scientist / Developer to conduct analytical activities supporting FCRM transaction monitoring systems.
- This is a highly collaborative, hybrid opportunity for a self-directed data science professional who can lead analysis, design, and support for technical data management solutions across projects of varying complexity.
- The successful candidate will use statistical and machine learning techniques to build and maintain transaction monitoring, customer behavior, and risk scoring models.
- The role also contributes to scenario performance tuning, model monitoring, documentation, audit support, and creative analytic solutions that strengthen the client's financial crime risk capabilities.
Experience: 8+ years of overall experience.
Responsibilities: AML Analytics & Transaction Monitoring
- Provide maintenance of transaction monitoring scenarios and analytical queries supporting strategic efforts to mitigate money laundering and terrorist financing risk.
- Conduct AML/ATF scenario performance tuning, machine learning execution, and model performance monitoring.
- Help maintain structured documentation that demonstrates an effective scenario review program for audit, compliance, and regulatory requirements.
- Assist with transaction monitoring questions from Global AML partners, audit, and other stakeholders with management support.
Responsibilities: Data Science & Technical Leadership
- Work independently as a senior lead across analysis, design, and support of technical data management solutions.
- Use statistical and machine learning techniques to build effective transaction monitoring, customer behavior, and risk scoring models.
- Design and lead iterative learning and development cycles that produce new, creative analytic solutions.
- Partner with FCRM teams to implement changes to existing solutions or alternative solutions and serve as an ambassador for data science.
- Identify improvement opportunities and provide data- and model-supported insights for business strategies.
Required Skillsets
- Predictive Modeling: Extensive experience building predictive models with machine learning methods.
- Core Stack: Expert Python, PySpark, Databricks, Azure, and SQL.
- Data Analysis: Expert data exploration, analysis, statistics, and data mining capability.
- Business Problem Solving: Ability to apply statistical techniques and data mining tools to practical business challenges.
- Senior Delivery: Ability to work independently, manage deliverables, and meet timelines.
Preferred Skillsets
- Databricks, programming, or statistics certifications.
- Java, Scala, or Tableau.
- AML knowledge and data-driven or data-centered project experience.
- Banking or financial institution experience.
- Strong written and verbal communication, interpersonal skills, organization, adaptability, and attention to detail.
Education: Bachelor's degree or higher in Statistics, Computer Science, or Software Engineering; a master's degree is preferred.
Certifications: Databricks, programming, and statistics certifications are assets.
Licenses: Not specified.
About US Tech Solutions:
US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit
www.ustechsolutions.com
US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring.
