• Model validation

• Modeling & Analysis: Architected the firm's portfolio performance analytics model in Python, leading to quantitative insights and forecasting capabilities• Data Integration & Processing: Automated and streamlined the data cleaning and processing workflow from Bloomberg and Fred sources using APIs in Python, enhancing efficiency and data accuracy • Data Management: Built and maintained database in PostgreSQL, ensuring market and client data’s integrity and accessibility for analytics • Client Reporting Automation: Designed, developed, and automated detailed client reports in Python, with Excel as interface • Innovative User Engagement: Recently devised and executed an online trial service, enabling users to effortlessly engage with our analytics model through QR code scanning, enhancing user experience and driving greater engagement.• Quantitative Portfolio Analysis: Delivered high-quality portfolio analysis for clients• Stakeholder Collaboration: Engaged proactively with diverse stakeholders and data vendors, guaranteeing data precision and its congruence with strategic business requirements.• SaaS Development: Led the development of a SaaS platform for our analytics model, integrating MSFT Graph API, Slack API, and AWS services 
(CloudWatch, S3), enhanced data accessibility and empowered both internal and external users to make data-driven decisions • DevOps: Successfully transitioned the software to AWS ECR by implementing a CICD environment, resulting in reduced deployment times and increased release efficiency.• Presentation: Showcased company's core models to potential partners, driving business growth

• Data-Driven Client Insights: Analyzed client behavior using transaction data, enhancing targeted marketing and sales efforts• Client Relationship Management: Facilitated client communications, addressing and resolving user concerns

• Cost-Efficient Restocking: Recommended a restocking strategy based on market research, yielded a remarkable monthly cost reduction of $3,000, enhancing the company's profitability• Data-Driven Customer Support: Enhanced customer service operations by consistently supplying data insights, resulting in more informed decision-making and improved client interactions