
As a Data Scientist at Meta, I work at the intersection of machine learning, causal inference & experimentation, and product development to drive impact across Meta's family of apps. I am currently working within Monetization focusing on CRM Automation/GenAI. Previously, I worked on the Shops Ads Ratings & Reviews team where I helped drive their goal-setting and roadmapping, and delivered key insights into Review quality.

## Key Impact

### Sales Intelligence - CRM Automation / Generative AI
* Developed Measurement and Work plans for subject area
* Developed production-grade machine learning model to improve sales intelligence insights in CRM

### Shops Ads - Ratings & Reviews
* Partnered with product managers and engineers to define north-star metrics and roadmap goals for ratings and reviews metadata, ensuring alignment across stakeholders
* Partnered with engineers and product managers to investigate negative trends in ratings and reviews weighted coverage. Identified pipeline bottlenecks and proposed remediation steps using Python & Presto, improving coverage by 10%
* Interpreted A/B test experiment results to assess new feature impact on primary success metrics resulting in ship decisions for the feature and a 3% increase in ROAS for the treatment group


## Technical Skills & Methods

* Applied advanced statistical methods including causal inference, Bayesian modeling, and time series analysis
* Built production-grade ML models using python, our internal pipeline infrastructure, and prestoSQL & spark SQL
* Created interactive data visualizations to communicate complex findings to technical and non-technical stakeholders
* Championed data-informed decision making through all stages of the product development lifecycle
* Collaborated with ICs, Ms, directors and a VP of Analytics to develop an analytics-focused training program on integrating AI-driven techniques (e.g., code generation, structured outputs, automation) into standard workflows, accelerating insight generation
* Currently designing self-service analytical notebooks using generative AI (fine-tuned LLaMA model) to interpret results. Potential effects include reducing time-to-insight for non-technical stakeholders, enabling on-demand exploratory analysis and freeing data scientist bandwidth for high-impact projects

My work has directly contributed to improving business metrics in key product areas, while maintaining a strong focus on user privacy and ethical data use principles. 


Data Scientist


As a Data Scientist at TikTok, I worked on developing and implementing internal tooling and scaled infrastructure for analytics and operations teams.

## Key Impact

* Implemented product-focused metrics in the Dashboard Measurement Framework to analyze internal tooling, leading to a 10% increase in stickiness and a 5% increase in penetration rate. This resulted in enhanced stakeholder satisfaction and improved XFN buy-in across the organization 
* Partnered with the Creator Management and Content Data Science teams to create a Partner Measurement tool and source-of-truth dashboards built using HSQL and ByteDance’s proprietary data visualization software. The tool created a holistic view of avenues for creator growth driving increases in DAU and engagement.

## Technical Contributions

* Led the Resource Measurement Project focused on creating 100% trust in data and alleviating YARN & HDFS resourcing challenges. Resulted in the creation of controls for process transformation, a 33% reduction in job failure rates, and the implementation of robust resource & data quality monitoring across 6 queues spread amongst 5 business areas including user insights, content programming, and creator management. This unlock led to prototyping of generative AI solutions for sentiment analysis and improved XFN trust in dashboard reliability

During my time at TikTok, I had the opportunity to work on cutting-edge data tools in interact with coworkers across many countries (and too many timezones). This reshaped how I viewed the power of information at scale.


Data Scientist / Business Analyst


As a Data Scientist / Sr. Business Analyst at Mathnasium, I helped build out the end-to-end creation of our analytics practice. When I arrived, most analytics was done out of a bloated excel spreadsheet - upon my departure 12 months later, (with the support of a fantastic executive leadership & IT team) analytics and information were democratized across the franchisor, franchisees, and all employees at the company with state of the art tooling at their disposal.

## Key Impact

* Designed & deployed a high-performance self-service business analytics & visualization reporting tool to a franchisee network of 1,000+ learning centers resulting in transformative improvement in franchisee analytics capabilities, accelerating a data-driven culture, and generating 1% lift in additional revenue for the franchisor
* Collaborated with executive stakeholders and traditional IT functions on the development of scalable data infrastructure and introduction of north-star metrics for company along with data governance documentation 
* Built machine-learning-based regression models with high train/test R2 to develop an understanding of successful market valuation and forecast average unit volume for current & future portfolios using Python (XGBoost). This highlighted the contrast between unit volume and projected volume resulting in increased franchisor revenue 
* Presented deep-dive analyses to executive leadership including reports on pricing/product mix, marketing initiative efficacy, and top-of-funnel characteristics using PostgreSQL, Python, and AWS QuickSight. Resulted in strategy shifts in marketing initiatives and franchisor-franchisee messaging

## Technical Skills & Methods

* Applied advanced statistical methods including causal inference and time series analysis 
* Created inital data governance documentation and metric designs
* Efficiently created operational data analyses for executive leadership and board meetings while laying the groundwork for more advanced analyses
* Created interactive data visualizations to communicate complex findings to technical and non-technical stakeholders
* Championed data-informed decision making through all stages of the student account lifecycle


Data Scientist / Sr. Business Analyst


As a Data Informatics Analyst at SCAN Health Plan, I worked on developing and implementing data visualizations, maintaining data pipelines, and providing ad hoc research.

## Key Impact

- Developed frequently-used dashboards dedicated to understanding the effects of interventions/outreach programs on medication adherence and compliance
- Worked with the CDO to develop and maintain a dashboard tracking high-visibility Diversity, Equity, and Inclusion iniatives throughout the 3,500+ person organization
- Performed ad hoc analyses and research reports

## Technical Contributions

- Implemented python translations of old SAS code, reducing technical overhead needed for manually refreshing data pipelines
- Collaborated with manager on identifying opportunities for automation of daily and weekly tasks

During my time at SCAN, I had the opportunity to work on an analysis of medication adherence for some of our members in at-risk communities. This study found important socio-economic factors that, when present, could help in identifying members, who may need additional support from our outreach specialists. What made this more unique is it could be accomplished without the use of PII or HPI, maintaining the privacy of each member while still generating robust results.


Data Informatics Analyst


Completed a Master of Science degree in Applied Analytics (the name has now transitioned to Business Analytics) at Pepperdine University's Graziadio School of Business.

STEM-designated program with a focus on advanced predictive and prescriptive analytics using machine learning in addition to business intelligence, analysis, and management. Courses focus on big data management, Python, R, SQL programming, database management, cloud management/distributed computing, and decision sciences.

## Academic Focus

- Maintained a 3.75 GPA 

## Projects & Research

- Implemented a novel approach to image classification using limited training data

## Organizational Contributions

- Graziadio Student Advisory Board: Chairman
- Emerging Technologies: Member

My education at Pepperdine provided the strong theoretical foundation and practical skills that launched my career in Analytics, AI, and machine learning, leading directly to opportunities in Data Science and beyond. 


M.S. in Applied Analytics


Completed a Bachelor of Arts degree in Psychology at Arizona State University.

This gave me a glimpse into the worlds of stats, psychology, sociology, and the intersections between them all. I also spent most of my academic career there studying for a finance major, but decided it was better to graduate during the pandemic than wait a year for the second degree.

My education at ASU provided the strong theoretical foundation in statistics, human behavior, and experimental design.


Professional Experience

Data Scientist

Data Scientist / Business Analyst

Data Scientist / Sr. Business Analyst

Data Informatics Analyst

M.S. in Applied Analytics

B.A. in Psychology

Interested in working together?