Data Scientist

Primary Responsibilities

• Analyze large, complex financial and client datasets to uncover trends, behavioral patterns, and investment signals.
• Design, validate, and operationalize predictive models to support asset allocation, risk assessment, and client segmentation strategies.
• Collaborate with business and financial stakeholders to translate strategic objectives into analytical approaches and data-driven solutions.
• Build and maintain scalable data pipelines and workflows, ensuring reliable data ingestion, transformation, and quality within Snowflake environments.
• Develop intuitive dashboards, visualizations, and reporting artifacts (Power BI, Tableau, or similar) to communicate insights and influence decision-making.
• Work closely with engineering teams to integrate analytical outputs and models directly into core wealth management systems and workflows.
• Maintain thorough documentation and uphold data governance, controls, and compliance standards aligned with financial regulations and industry best practices.

Qualifications

• Master’s degree in Data Science, Computer Science, Statistics, Finance, or a related quantitative field.
• Hands-on experience in Data Science, ideally within financial services, wealth management, investment analytics, or related sectors.
• Strong programming experience in Python or R for statistical modeling, machine learning, and data manipulation.
• Proficiency in SQL and demonstrated experience with Snowflake for data warehousing and analytical workloads.
• Solid understanding of financial instruments, portfolio construction, performance metrics, and risk modeling concepts.
• Experience with data visualization and business intelligence platforms (e.g., Tableau, Power BI, Looker).
• Familiarity with cloud environments (AWS, Azure, or GCP) and modern data stack components.
• Exceptional communication and stakeholder engagement skills, with the ability to convey complex concepts to non-technical audiences.
• Experience with OpenAI function calling, MCP servers, or custom agent integration
• Experience with Retrieval-Augmented Generation (RAG) and vector database expertise
• Experience working with modern LLMs (OpenAI, Anthropic, Google)