Data Scientist (Generalist)
2019 - 2020Client (via Toptal)- Developed end-to-end marketing data and analytics solution including scraping, fusion, predictive modeling, deployment, reporting, and evaluation.
- Completed studies and proofs of concept for company leadership and regularly advised at that level.
- Built a hybrid statistical/NLP model for the impact of online reviews.
- Created a qualitative analysis framework to develop coding schemes for survey responses.
- Developed a predictive model for an all-in cost of delivery to a customer using years of historical data.
- Collaborated on a product-pricing model for a prototype-phase eCommerce application.
Technologies: Amazon Web Services (AWS), Matplotlib, Keras, TensorFlow, Scikit-learn, Pandas, SQLAlchemy, SQL, ECS, AWS, PythonCo-founder | Vice President of Data and Analytics
2017 - 2019Rubota Corporation- Collected and integrated data from disparate sources into a unified model.
- Worked with the chief engineer to develop a platform data model.
- Integrated in-house and third-party entity analytics.
Technologies: Machine Learning, PythonData Scientist
2014 - 2016Thetus Corporation- Produced prototypes and handled third-party integrations.
- Engaged with customers to understand their data and applications.
- Supported sales and marketing with demonstrations tailored to target customers.
Technologies: Amazon Web Services (AWS), PythonPostdoctoral Researcher
2009 - 2014Cornell University- Authored eight peer-reviewed studies in X-ray science, structural biology, and statistical mechanics.
- Developed novel analytical and visualization tools to investigate protein conformational motions.
- Managed teams running experiments at Cornell’s X-ray source and Argonne National Lab under extreme time pressure (typically 24 to 48 hours from start to finish).
- Built and maintained data pipelines to construct 3D models of macromolecules from 1000s of X-ray images.
Technologies: Linux, PythonGraduate Research Assistant
2004 - 2009Cornell University- Pioneered experimental techniques to exploit opportunities in the rapidly-evolving field of structural biology.
- Standardized and automated existing data collection and processing practices resulting in a greatly increased impact of the final product.
Technologies: Linux, Python