- Architecting production level ML workloads for Databricks customers in the Financial Services vertical, using the unified platform, including end-to-end ML pipelines, training/inference optimization, integration with cloud-native services and MLOps- Providing advanced technical support to Solution Architects during the technical sale ranging from feature engineering, training, tracking, serving to model monitoring all within a single platform, and participating in the larger ML SME community in Databricks- Collaborating with the product and engineering teams to represent the voice of the customer, define priorities and influence the product roadmap, helping with the adoption of Databricks’ ML offerings- Serving as the trusted technical advisor for customers developing GenAI solutions, such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, content generation, and monitoring

- Developed state-of-the-art machine learning solutions, driving client business growth and risk mitigation.- Spearheaded an enterprise-level effort to create Large Language Model (LLM) based applications for internal and external client use cases, including document semantic search, database question-and-answer, structured and unstructured information retrieval, etc.- Built and deployed a sophisticated investor lifetime value prediction model, accurately determining user investment longevity and profitability, which resulted in improved investment strategies.- Developed Apex's inaugural ACAT fraud detection model, which identified and mitigated potential fraudulent asset transfers, successfully safeguarded investor assets while reducing false alarms.

- Developed the first churn risk prediction model for Apex clients, to help them target users that are at higher risk of leaving- Developed the first data-driven investor segmentation model through clustering on investor trading behaviors and demographic features- Developed Apex’s time series forecasting framework, which is used to predict
 internal and client-specific metrics and key performance indicators (KPIs), such as daily account openings and closures, daily trading volumes, and daily investor money movements- Developed a nightly batch processing runtime
 prediction model that helps prevent excessive batch runtime that breaches client service-level agreements

- Responsible for designing, building, and deploying machine learning (e.g. scikit-learn, xgboost) and statistical forecasting (e.g. statsmodels, fbprophet) models to predict store demand, understand traffic behavior, and optimize labor efficiency- Developed global store-level daily demand forecast models, which were integrated into the labor scheduling engine, that has improved scheduling accuracy by 15% (WMAPE), leading to an estimated revenue lift and cost savings between $15 million to $25 million annually- Designed a labor optimization model that compares individual store service-level potentials to those of similar stores (based on clustering algorithms) for the goal of maximizing sales-per-traffic- Created and deployed a labor scenario-planning tool, using gradient boosting ensemble model, that enables Nike store leadership to conduct quarterly labor budget and headcount planning- Built a suite of workforce intelligence dashboards that are used daily by Nike store leadership across more than 250 Nike Direct stores in the North America region- Maintaining value-driving relationships with key stakeholders, including retail workforce operations, supply and demand planning, and finance teams

- Responsible for leading the research and analysis of highly confidential initiatives- Designed and deployed Monte Carlo simulation models to predict asset growth- Influenced executive decision-making through compelling analyses and effective data visualizations- Performed periodic global fund complex growth forecasting and analysis- Led product post-launch growth projection and analysis- Performed product go-to-market research for new fund launches- Contributed to ongoing competitive landscape analysis on multi-asset investment fund categories

- Investment management and investment operation audits- Due diligence field assignments

- Rigorous 13-month leadership development program- Financial planning & analysis (FP&A)- Investment advice methodology and portfolio analysis- Internal risks and controls assessment- Global securities lending

- Equity trading back office functions