Professional Highlights

AlixPartners LLC | Analyst [Contract]

At AlixPartners, I focused on optimizing global staffing operations by turning disconnected planning processes into a unified analytics system. My role involved collaborating with Finance, Delivery, and Talent teams to deliver scalable solutions that improved workforce utilization, reduced reporting friction, and helped leadership make faster, data-backed decisions.

  • Built a forecasting-based headcount model using Python and SQL that improved consultant utilization by 40%.

  • Designed and executed A/B tests to evaluate onboarding and placement efficiency, influencing allocation strategy across practices.

  • Developed reusable Alteryx workflows and automated AWS Glue pipelines, reducing operational reporting time by 35%.

  • Re-architected Snowflake schemas and partitioning logic to cut query times and improve dashboard performance.

  • Created a variance detection engine that flagged $1.2M+ in billing discrepancies, tightening financial accuracy.

  • Delivered a Power BI self-serve analytics hub adopted by 50+ stakeholders, reducing ad-hoc requests by 40%.

  • Led standardization of KPI definitions and reporting logic to improve consistency across analytics and business teams.

Huemanite LLC | Data Scientist [Contract]

At Huemanite, I was brought in to own the full analytics stack for a product and marketing team focused on user retention and conversion. The environment required lean, highly available systems and fast turnaround on insights. I developed everything from robust ETL to self-service reporting, helping the team scale its operations and reduce churn with data-driven actions.

  • Led analytics across onboarding and retention, driving a 15 percent increase in activation.

  • Built fault-tolerant Airflow DAGs with SLA-based alerting and recovery logic, reducing ETL failures by 60 percent.

  • Refactored dbt models in Redshift to improve dependency flow and cut build times by 35 percent.

  • Created interactive Power BI dashboards with advanced DAX to monitor user behavior, reducing ad-hoc requests by 25 percent.

  • Implemented data governance controls via AWS Glue and S3 versioning to ensure compliance and traceability.

Arizona State University | Data Analyst [Capstone]

Led a comprehensive data analysis project involving over 8 million datasets to support STEM policy reform. Utilized advanced Python libraries (Pandas, NumPy) and Excel with VBA for complex data processing, applying ORM techniques for efficient data transformation and analysis.

  • Enhanced Data Accuracy by 20% by implementing rigorous data quality protocols and automated data cleaning processes, using SQL stored procedures and Python scripts to ensure reliable datasets for analysis and forecasting.

  • Created Interactive Tableau Dashboards to visualize key metrics and trends, leveraging custom SQL queries for data extraction. These dashboards provided clear, actionable insights, supporting strategic decision-making in STEM policy reform.

ChainLink Research | Data Analyst [Internship]

At ChainLink, I supported a high-traffic content platform by optimizing streaming data pipelines and surfacing actionable user insights. My role spanned backend ingestion to dashboard delivery, helping the team respond to spikes in demand and improve reader retention through behavioral segmentation.

  • Tuned Kafka ingestion pipelines with Python and SQL, reducing latency by 30 percent for real-time analytics.

  • Built scalable ETL pipelines from Kafka to Snowflake, improving performance and cutting dashboard load times by 40 percent.

  • Set up SLA-based alerting using CloudWatch, reducing incident response time by 40 percent.

  • Analyzed 100K+ user sessions to uncover drop-off trends, reducing churn-related costs by 15 percent.

  • Delivered Tableau dashboards highlighting content KPIs and engagement metrics for the editorial team.

Synobiz Solutions | Junior Data Scientist [Full-Time]

At Synobiz, I worked across data science and engineering teams to build ML models, design real-time pipelines, and support financial analytics. I contributed to the full cycle of feature engineering, model training, and delivery of dashboards that drove business decisions across customer retention and forecasting.

  • Developed ML pipelines using XGBoost and SHAP, improving churn prediction accuracy by 20 percent.

  • Implemented real-time feature ingestion using AWS Kinesis and Snowflake, bringing latency under 10 seconds.

  • Optimized PySpark batch jobs to improve cluster stability and throughput.

  • Built production-grade feature stores to streamline model development and cut processing overhead by 15 percent.

  • Created Power BI dashboards to support revenue forecasting and financial planning, increasing adoption by 30 percent.

  • Led architectural alignment across ML, finance, and engineering to define data lineage and recovery protocols.