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)