Experience – Robert Alward

McKinsey and Company | Data Scientist [2022-2024]

Led the development of an AI market review, showcasing advanced competencies in artificial intelligence, machine learning, and digital innovation.
Pioneered a dynamic pricing strategy using CART algorithm for a major industrial distributor, optimizing customer-level pricing.
Utilized GenAI tools including LangChain, Azure, and OpenAI API to develop a voice automation application for utility services.
Played a key role in developing an Agent Software Development Kit for interactive agents using the GenAI backend, enabling more flexible usage and development.
Conducted comprehensive evaluations of backend features (Azure, AWS, GCP) for speech-to-text, llms, and text-to-speech technologies, significantly enhancing app functionality.
Developed a comprehensive digital twin simulation for a global payments company, aligning parameters with real-world scenarios.
Demonstrated leadership in AI-based solutions and client management, delivering high-quality results and client satisfaction.
Created a customer-centric predictive pricing model, managing large and messy data sources, and aimed at robust performance for 1-2 years.
Spearheaded code error reduction efforts, enhancing program sustainability and readiness for production.
Transferred models to new data scientists with clear documentation, detailed walkthroughs, and client context, ensuring seamless knowledge transfer.

Numer.ai | Data Science Competitor [2022]

Compete in a distributed quantitative hedge fund competition using machine learning to predict the performance of anonymized global equities
Used a XGBoost model with custom training loops and unique time-series based predictors to develop a competitive model and consistently improve in competition ranking and returns

Used Docker to parallelize large scale web-scraping reducing run time by 83% and failed runs by ~66%
Recognized and engineered 3 new features using Python and React improving user experience and post-run analysis
Enhanced previous research on public education using web-scraping and NLP to highlight demographic disparities

NASA JPL | Geospatial Data Scientist [2021]

Researched and developed 6 models to predict Ozone levels from earth observation data using python and Scikit learn
Utilized back trajectory models for particulate matter integrating HYSPLIT, meteorological data, and earth observations resulting in unique research insights which served as a guide for the 10 week project
Collaborated to deliver a software product to automate NASA satellite data visualization and analysis

Globally Unified Air Quality (GUAQ) | Lead Startup Data Scientist [2020]

Designed and carried out comprehensive quality assurance tests to ensure GUAQ monitors ability to perform at industry grade accuracy levels under all conditions
Developed and implemented an algorithm to transform raw data into AQI metrics for GUAQ’s consumer app
Ran analysis using machine learning techniques in R to identify anomalies within GAUQ devices data streams
Uncovered data reliability issues and implemented algorithmic adjustments to increase precision and accuracy of data

Georgetown Entrepreneurship | Operations Analyst [2019]

Lead the reimagination of the Georgetown Venture Lab as it pivots to an accelerator model, driven by insights from customer interviews, in-depth market analysis, and analysis of comparable programs

Optimized the Venture Lab by increasing the number desks within the space from 85 to 106 resulting in a $5,250 increase in the monthly revenue ceiling

Designed and implemented an undergraduate internship program matching 21 interns with 9 companies

EDUCATION

Georgetown University