David Huang Email & Phone Number

Portland, Or, Us

San Francisco, Ca, Us

- 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

Dallas, Texas, Us

- 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.

Dallas, Texas, Us

- 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

Beaverton, Or, Us

- 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

Valley Forge, Pa, Us

- 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

Valley Forge, Pa, Us

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

Valley Forge, Pa, Us

- 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

Valley Forge, Pa, Us

- Equity trading back office functions

Beijing, Beijing, Cn

Master Of Science - Ms, Business Analytics

Bachelor Of Science, Finance