Senior Manager - Forensic Analytics-Anti-Money Laundering
- Oversee and lead multidisciplinary teams of data analytics professionals, software engineers, and business analysts to design, develop, and deliver sophisticated data-driven solutions addressing critical regulatory, risk, AML, and financial crime challenges.
- Shape project strategy and execution, employing intelligence-led and innovative approaches-such as advanced transaction monitoring, model development and validation, model risk management, and tuning-to meet evolving global regulatory requirements.
- Direct utilization and optimization of platforms including Actimize, Oracle FCCM, LexisNexis Fircosoft, Bridger, SAS Viya, SAI/Norkom, and other leading technologies.
- Build and maintain strong client relationships, serving as a strategic advisor to C-level executives and key stakeholders to ensure the effective translation of analytical solutions into actionable business insights and regulatory compliance recommendations.
- Drive day-to-day engagement management, including solution architecture, scope and resource planning, delivery operations, risk and issue management, and end-to-end quality assurance.
- Champion capability development by fostering technical skill-building, innovation, and continuous learning within the team, and by mentoring managers and staff.
- Utilize cutting-edge Machine Learning (e.g., XGBoost, Ensemble), GenAI, and advanced analytics techniques to solve complex client problems and discover new business opportunities.
- Lead business development initiatives, including proposal development, client presentations, and thought leadership to expand Deloitte's marketplace eminence.
- Provide strategic input on technology investments and practice development initiatives to align with the firm's roadmap and growth objectives.
- Rigorously track performance metrics, including utilization, budget versus spend, and resource allocation, ensuring project and practice objectives are achieved.
- Bachelor's degree from an accredited university; advanced degree preferred.
- 12+ years of hands-on technical and leadership experience in mining and analyzing complex electronic transactional data, with deep proficiency in programming languages such as SQL, Oracle, Python.
- Demonstrated track record in managing large-scale forensic analytics or financial crime compliance programs, with extensive experience in AML, KYC, Transaction Monitoring, Sanctions, and regulatory risk analytics.
- Expertise in data analytics tools (Power BI, Tableau), machine learning, and model development within AML, compliance, or risk management contexts.
- Significant experience with enterprise-level platforms (Actimize, Oracle, LexisNexis Fircosoft, SAS Viya), and familiarity with data engineering tools (Informatica, Kafka, Snowflake, Hadoop).
- Superior communication and presentation skills, with the ability to engage effectively with senior client executives, regulators, and cross-functional internal teams.
- Proven ability to balance and prioritize multiple, concurrent projects and delegate effectively to ensure timely and successful delivery.
- Demonstrated commitment to personal and professional growth, as well as to the development and mentorship of peers and junior staff.
- High level of integrity, initiative, and professional judgment.
- Limited immigration sponsorship may be available.
- Ability to travel up to 25%, based on engagement requirements.
- Advanced degree in a quantitative, technical, or business discipline (e.g., Computer Science, Statistics, Mathematics, Business Administration, Finance).
- Extensive experience managing large project teams and shaping strategy for forensic analytics or financial crime engagements.
- Deep expertise in advanced analytics techniques including sampling, optimization, logistic regression, cluster analysis, Neural Networks, Decision Trees, and both supervised and unsupervised machine learning.
- Demonstrated experience in business development, thought leadership initiatives, and building practice eminence.
- Knowledge of data governance, data lineage, testing, and regulatory technical requirements.
- Exposure to a variety of financial industry data sources, including wire transfers, trade finance, and broker/dealer environments.
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