Lance W Email & Phone Number

San Francisco, California, United States

 Developed and implemented a real-time fraud detection system using machine learning models, reducing fraudulent transactions by 30%. Led a team that integrated predictive analytics into customer service platforms to personalizecustomer interactions and increase customer satisfaction. Collaborated with the IT and data warehousing teams to streamline data collection and preprocessing, enhancing the performance of analytics models. Utilized Generative AI for automated customer response generation, improving response timesand satisfaction rates. Initiated and led a pilot project on the application of Large Language Models (LLMs) forenhancing risk assessment tools, which improved the prediction accuracy of credit defaults. Published research on the application of AI in risk assessment, presented findings at severalindustry conferences. Utilized natural language processing techniques to analyze customer feedback, leading toimprovement in customer service ratings. Developed predictive models to assess credit risks, reducing financial losses through identification of key risk factors in loan approval processes. Applied time series analysis to forecast financial trends, assisting the treasury with asset management and investment strategies. Regularly presented insights and strategies to stakeholders, translating complex data findingsinto understandable business terms to support strategic planning. Kept abreast of industry trends and technologies by attending workshops and certifications inadvanced analytics and machine learning applications.

San Jose, California, United States

 Developed machine learning models to predict market trends and optimize eBay's pricingstrategies and inventory management. Used Python for main development and R for statistical analysis. Utilized Redshift for data warehousing and PostgreSQL for transactional databases. Deployed models and manage data using AWS services like SageMaker for model training, S3for data storage, EC2 for computing resources, and Redshift for analytics. Integrated natural language processing to improve search functionalities and productcategorizations, significantly enhancing user experience. Tracked and managed project milestones using Jira and Rally to ensure agile project execution. Used Tableau for creating dynamic reports and dashboards to visualize the results of dataanalysis and model predictions. Focussed on Data Mining, Machine Learning, Data Visualization, Analytics, AI, Model Building,Data Ingestion, and Feature Engineering. Employed both supervised and unsupervised learning methods including regression (linear andlogistic), decision trees, bagging, XGBoost, KNN, Naïve Bayes, and PCA. Utilized TensorFlow, Keras, TFX, and PyTorch for developing and deploying deep learning models. Leveraged Scikit Learn, Pandas, Numpy for data manipulation, along with Jupyter Notebookand Spyder as development environments. Developed and refine algorithms for feature extraction and dimensionality reduction to improve model accuracy. Continuously test and refine models using real-world data and feedback loops to enhanceperformance. Worked closely with data engineers, business analysts, and other stakeholders to integratemachine learning solutions effectively.

New York, United States

 Implemented advanced analytics that increased content engagement by 30% throughoptimized targeting and personalization. Developed predictive models using machine learning algorithms to forecast user behavior andtrends, significantly improving the accuracy of targeted marketing campaigns. Designed and analyzed numerous A/B testing frameworks to evaluate the impact of differentcontent strategies, enhancing user engagement rates. Deployed machine learning models into production, using Python and Spark to process andanalyze large datasets in real-time. Worked with big data technologies including Hadoop and Spark to handle complex dataprocessing tasks for enhanced decision-making. Created dynamic dashboards and reports using Tableau and Power BI to visualize businessmetrics for stakeholder presentations. Team Collaboration: Led a team of junior data scientists and analysts to build and implementscalable data models for strategic initiatives. Business Strategy: Worked closely with the marketing and business strategy teams totranslate data insights into actionable, quantifiable plans that drove content and advertisingrevenue growth.

Las Vegas, Nevada, United States

 Developed and deployed python backend systems and machine learning models for variousapplications, including predictive maintenance, customer segmentation, and demand forecasting. Collaborated with cross-functional teams to gather requirements, design scalable backendsolutions, and implement scalable data pipelines using technologies like Apache Spark, Hadoop, and AWS Glue. Utilized Python for data manipulation, model development, and deployment, leveraginglibraries such as NumPy, Pandas, Scikit-Learn, TensorFlow, and PyTorch. Also, libraries andframeworks such as Django and Flask for backend development. Integrated machine learning models with cloud services (AWS, Azure, GCP) and deployed themusing Docker containers and Kubernetes. Ensured model performance and scalability by implementing monitoring and optimizationstrategies. Also ensured backend systems' security and performance through regular testingand optimization.