San Francisco Bay Area
Technical Project and Product Manager in medical devices with experience in Data Engineering and Data science. Proficient in SQL and Python for analytical problem solving, predictive modeling, data mining. Experienced in data wrangling, exploratory data analysis, visualization of large amounts of data, and interpretable ML. Applies a diverse technical knowledge from nanotechnology to data science and artificial intelligence to develop innovative products to meet business objectives; invested in staying apace, adapting with technical trends, and applying learning. Effectively collaborates with executives and engineering teams to bring disparate perspectives into a cohesive vision. Technical Skills: - Medical Devices: Design Control, FDA Regulation, Requirements Management (Codebeamer), Enzyme QMS - Product & Project Management: Agile, Scrum, Phase Gate, GANTT, Jira, Confluence, Asana, Notion - Databases: SQL (Snowflake, PostgreSQL, dbt, Amazon RDS, ORACLE, MariaDB), Google Cloud BigQuery - Languages: Python (Pandas, Scikit-Learn, NumPy, Requests, Beautiful Soup, SHAP), MATLAB, SQL, C - Predictive Modeling: Linear/Logistic Regression, Naïve Bayes, k-Nearest, Support Vector Machines - Machine Learning: Classification, Clustering, Decision Tree, Random Forest, Gradient Boosting - Data Visualization: Matplotlib, Seaborn, Jupyter, Flourish.studio - Tools: Git, GitHub, Docker, APIs, VS Code, MS Office (incl. Excel) - Data Science Methods: Mining, Wrangling, Cleaning, Analysis, Visualization, Storytelling
+ Provide subject matter expertise on Artificial Intelligence (AI) and Digital Transformation + Supervise 70-100 managers and executives each quarter though the AI and Digital Transformation certificate programs leading them to apply the learnings in their organizations. + Encourage and engage participants in live teaching sessions, on discussion boards, and in weekly office hours +Tailor complex technical messages to executive and manager-level audience from different industries and backgrounds, supplementing the course content with case studies and intuitive examples
+ Developed tools & dashboards that allow Process Engineers monitoring and analysis of large-scale manufacturing data for the production of pharmaceuticals for treatments of COVID-19 and cancer. + Supported the migration from legacy lab ticketing system to cloud-based solution boosting efficiency. + Built features & pipelines for company-wide data science initiative to increase process yield by 2%. + Maintained data warehouse that ingests time-series and transactional data from multiple sources.
+ Developed interpretable explanations of the health risk score with local interpretable explanations (LIME) and Shapley values (SHAP) to increase readability and trust in the model by healthcare providers. + Built models with Sci-Kit Learn, XGBoost, and Light GBM to provide patients with a health risk assessment score, based on questionnaire data extracted from a SQL database. + Identified, built, and deployed custom healthcare news scrapers using Python and Beautiful Soup. + Authored a Machine Learning training for physicians to promote transparency and trust in ML models.