Abby L. Email & Phone Number

California, United States

San Diego, California, Us

1. Led a team of data scientists in the development and deployment of three multi-touch marketing attribution models subsequently elevated the efficiency of marketing and sales operations.2. Developed an advanced marketing lead scoring model utilizing machine learning techniques, doubling both recall and precision.3. Leveraged FinBERT to embed B2B firmographic data and synergize it with organic search data, enabling the creation of unique prospect clusters for personalized marketing campaigns. This resulted in a 10% uplift in F1 score by incorporating cluster results into the propensity model.4. Utilized advanced unsupervised learning techniques to refine a fuzzy match algorithm, leading to a 20% increase in match rates across various marketing analytics platforms and thereby boosting overall productivity.5. Guided a team of data engineers in the design and execution of a suite of multifaceted analytical dashboards, dramatically transforming and streamlining the data-driven decision-making landscape.

Sydney, Nsw, Au

1. Developed a purchase propensity model using LightGBM to predict each free instance likelihood to convert to paid.2. Identified six segments with a large decline in purchase probability using decision tree and proposed a list of recommendations to address the decline in new customer growth within those segments.3. Collaborated with multi-functional teams to identify pain points during the customer onboarding journey.4. Discovered multiple opportunities to enhance top of funnel quality and accelerate monetization.

Eveleigh, New South Wales, Au

1. Led a team of data scientists in maintaining transaction enrichment product that cleanse billions of transactions from millions of retail customers using advanced language model for one of the top five US banks.2. Led a team of data scientists in a machine learning proof-of-concept that developed multiple uplift models using Gradient Boosted Trees to optimize customer response to marketing strategies.3. Designed the data architecture and requirements for the bank’s new personalization engine that connects to millions of customers. Collaborated with functional heads to improve operational processes for data ingestion into the Enterprise Data Lake.4. Utilized NLP techniques to analyze call center transcripts and developed machine learning models that generated personalized topic recommendations for call center agents.5. Led a team of three data scientists in deploying a Gradient Boosted Tree model to forecast RX sales and optimize inventory replenishment strategies for one of the world's largest retailer.

Sydney, Nsw, Au

1. Managed and directed the Personal Lending account management team responsible for driving growth of consumer lending products. Led a team of three credit risk analysts in developing and executing strategic initiatives to improve loan performance and expand market share.2. Implemented a new application strategy, offering customers personalized loan amounts and terms that better suited their needs.3. Leveraged outlier detection techniques to identify opportunities to reclaim customers who were previously declined due to inaccurately entered financial information.

Eveleigh, New South Wales, Au

1. Employed clustering techniques to create detailed customer profiles based on demographics and shopping behavior, enabling retailers to perform personalized targeting within each segment for improved marketing effectiveness.2. Utilized a parametric Poisson process model to accurately predict the probability of out-of-stock occurrences at retail stores, allowing for proactive inventory management.3. Developed a GLM model to forecast sales uplift and categorize incrementality from sales promotions, resulting in optimized promotion strategies for retailers and suppliers.

Brisbane, Brisbane, Au

1. Refitted motor insurance risk model across three major brands.2. Identified segments with significant year-on-year premium increases using decision trees after a rate refresh.3. Fine-tuned propensity model relativities to ensure business KPIs are met.4. Conducted post-implementation monitoring of premium levels, strike rates, business mix, and model performance.

Bachelor Of Commerce (Degree With Honours), Actuarial Studies

Master'S Degree, Computer Science