Functional Consultant
3 weeks ago
Drive analytics and AI initiatives in Banking analytics and Capital markets with use cases ranging from Revenue generation, cost savings or risk management.
Engage with client to understand their specific needs and leverage past experience of building ML models in cross sell/up sell, customer lifecycle/LTV, EWS, Fraud prevention, Attrition, Segmentation, NBA/NBO, Marketing spend optimization, etc
Utilize AI ops practices, containerization, and cloud-based technologies to develop and deploy machine learning models at scale, enabling efficient queue management and auto-scaling capabilities.
Engage directly with clients to define project scope, gather requirements, and design AI solutions tailored to address specific challenges in the U.S. financial market.
Oversee the lifecycle of project development, from data collection and model building to deployment and implementation, ensuring quality and compliance with industry standards.
Manage and mentor a team of junior consultants and data scientists, promoting a culture of learning, collaboration, and professional growth.
Keep up-to-date with the latest advancements, technologies, and regulatory changes in the U.S. financial markets to continuously enhance SiriusAI's service offerings. Demonstrate and be eager to pick up new technologies like Generative AI and imbed them in the solutions
Develop comprehensive proposals and presentations for client engagements, effectively communicating AI-driven solutions and their impact on business outcomes.
Job Requirements:
A minimum of 4 years of experience in the financial services industry, with a focus on both banking and consulting. Specific exposure to the U.S. market is required. Experience working within banks, or top-tier consulting firms is highly preferred.
Strong functional expertise in banking industry and captives analytics, with additional knowledge in at least one of the following areas: Financial Risk Management, Compliance Management, Capital Markets, Wealth Management, or Asset Management.
Proven experience in developing, deploying, and managing machine learning models using AI ops practices, containerization (e.g., Docker, Kubernetes), and cloud platforms (e.g., AWS, Azure, Google Cloud).
Proficient in data science tools and programming languages such as Python, SQL, or similar, with the ability to manage and analyze large datasets using quantitative analysis and statistical methods.
Demonstrated experience in leading teams and managing complex projects, with strong organizational, leadership, and communication skills.
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