Senior data scientist responsible for developing and implementing end-to-end commercial machine learning models to facilitate big data analytics and the extraction of meaningful patterns. Developed software in Python and R statistical computing.

Senior research scientist in charge of designing and developing algorithms and machine learning solutions to solve well integrity and imaging challenges in the oil and gas industry. Implement and build software using Matlab and Python. Engaged with the engineering team to ensure software design met stakeholders’ expectations.► Collaborated with engineers to ensure logging tools met stakeholders’ expectations.► Implemented a real-time algorithm to estimate the casing-to-cement bond condition and tubing eccentricity.

Independently design and implement commercial machine learning solutions for solving interpretation challenges in big data analytics. Developed applications in Python and R statistical computing.► Prepared and delivered technical training classes to groups.► Provided support at major corporate accounts, provided tutoring sessions, and delivered workflows.► Worked closely with the sales team to produce tailored reports for clients.► Implemented classification engine for seismic lithofacies using imbalanced data learning, automated machine learning, and boosted decision trees techniques. ► Built classification engine for wireline lithofacies using imbalanced data learning, automated machine learning, and feedforward neural network techniques.► Implemented regression engine for wireline data using data discretization, imbalanced data learning, Gaussian process for data augmentation, and boosted decision trees techniques.► Built clustering engine for seismic attributes using automated machine learning and community detection techniques.► Implemented procedure to replace missing data in wireline logs using multiple imputation statistical technique.► Implemented procedure for automated classifier selection using automated machine learning and self-organizing maps techniques.► Built feature ordering procedure to enhance classifier/regression engine performance by combining conditional mutual information and PageRank algorithms.► Built dimensionality reduction procedure for wireline data to feed classification/regression engine based on entropy and symbolic representation.► Collaborated with developers to ensure software quality design and gains in code quality.

Advised and provided technical support to major corporate accounts, including Repsol, Chevron, HESS, ENI, Devon, and Shell. Coordinated data migration. Provided training and mentoring from junior to senior staff. Handled classes with 6+ students. Participated in software design development quality assurance. Engaged with customers within corporate accounts to drive software adoption.► Delivered technical training and mentoring, maximizing software usage and adoption by seismic interpreters.► Improved efficiency of the customer service department by reviewing/editing existing training manuals, providing training for new hires, and managing on-site consultants.► Provided on-site support at major corporate accounts, provided tutoring sessions, and delivered workflows.► Conducted applied research on clustering, data integration, and visualization for reservoir characterization. Published results in peer-reviewed journals. ► Built sustainable relationships with major corporate accounts, increasing trust and growing business opportunities by more than 20%. ► Supported seismic interpreters in finding reservoirs by routinely enhancing seismic interpretation workflows, seismic attributes analysis, and approaches to mentoring. ► Collaborated with developers to ensure software quality design and gains in code quality.► Ensured seismic interpreters took full advantage of Paradigm's technological solutions by coordinating and assessing data migration from Landmark, Geoframe, and Petrel.► Cultivated excellent relationships with account managers and corporate accounts, facilitating the implementation of solutions to address software deployment and data management concerns.