Sarah Reitmeister

Led the research, development, and deployment of over 10 bespoke machine learning applications to address the complex business needs of investment wholesalers:

• Workshopped with key stakeholders to identify pain points in their workflow and invent custom solutions.
• Transformed highly disorganized client Salesforce data into compelling visualizations and actionable insights.
• Implemented a data validation functionality notifying wholesalers if their vital SEC data is outdated, improving accuracy by up to 20%.
• Founded a lead scoring methodology which autonomously ranks up to 700k prospects across hundreds of customizable sales initiatives in real-time.
• Engineered an XGBoost algorithm leveraging call log sentiment analysis to predict prospects' likelihood to accept a sales meeting.
• Built a customer retention model using time-series transaction data to predict imminent redemptions and empower loss prevention.

• Automated complex ETL jobs in AWS Glue and engineered a marketing content ingestion Lambda, which uses spacy NLP to identify investment-related themes in 20k+ emails daily.
• Created and scheduled more than 50% of all company datasets in dbt and Airflow, including internal fact tables, Tableau dashboard models, and deployment-ready feature stores.
• Researched and engineered new features for 3 existing securities sales prediction algorithms, increasing overall performance by more than 25% each.
• Presented experiment designs and outcomes, algorithm efficacy metrics, and innovative product proposals to C-suite executives.
• Crafted custom visualizations to translate highly technical concepts into compelling narratives digestible by nontechnical audiences.

• Single-handedly facilitated ERP data migration for a Fortune 500 aerospace manufacturer during a multi-billion dollar acquisition.