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Experience

Charter Communications

Mar 2023 - Present
Sr. Business Analyst · St. Louise, MO
Data Operations & Impact Analysis
  • Led comprehensive impact assessments across all levels of system changes—ranging from code modifications and infrastructure updates to configuration changes. Worked closely with development and operations teams to identify critical risks that could affect system stability and user experience, ensuring all potential disruptions were addressed before release.
  • Monitored and tracked ongoing system-level changes such as new feature flags, data pipeline modifications, and API updates. Proactively assessed their potential downstream effects on various business-critical systems, including Tableau dashboards and telemetry data. Alerted and collaborated with relevant stakeholders, including product, operations, and platform teams, to ensure the seamless integration of these changes without compromising performance or stability.
  • Designed, developed, and maintained interactive Tableau dashboards that provided real-time insights into the operational impact of ongoing and upcoming releases, including effects on ordering systems and other key business areas. Used these dashboards to track KPIs, identify performance bottlenecks, and inform decision-making.
  • Documented and communicated system changes across the organization, ensuring that teams were fully informed of upcoming changes, including new configurations, database updates, or API changes. Developed clear, actionable release notes and status updates to keep all stakeholders aligned and mitigate any unforeseen impacts.
  • Collaborated cross-functionally with multiple teams (development, platform, operations, and product) to ensure that system changes were aligned with business goals, and coordinated release cycles to maintain system integrity, minimize downtime, and maximize efficiency.

    • University of Texas Arlington

    Jan 2021 - Feb 2023
    Research Assistant · Arlington, TX
    Exploring Deep Learning in Finance
  • Designed a state-of-the-art attention-based LSTM transformer encoder to capture complex dependencies in financial market signals. This architecture improved the model’s ability to forecast market movements by focusing on the most influential features in time series data, enhancing predictive performance for market forecasting applications.
  • Created a robust data sampling strategy using dollar volume and event-based triggers to ensure that the most relevant data points were selected for training the model. By focusing on high-value trades and significant market events, this approach reduced noise and improved model accuracy, allowing for more reliable predictions of market trends and events.
  • Integrated the Triple Barrier methodology to label market signals dynamically, providing clear buy, hold, or sell recommendations. This framework optimized trading positions, balancing risk and reward, and helped in maximizing risk-adjusted returns. As a result, profitability improved significantly by adapting to market volatility and managing downside risk effectively.
  • Spearheaded the development of a custom neural network in PyTorch and utilized Optuna for hyper-parameter optimization, which fine-tuned the model's performance. This process led to a 60% reduction in training time and a 9% increase in prediction accuracy, improving the efficiency of the model and enabling faster iteration and deployment.

    • Education

    MS, Industrial Engineering

    3.9 GPA
    University of Texas at Arlington
    Design of Experiments, Statistical Methods, Machine Learning

    BE, Mechanical Engineering

    Bachelor of Engineering