Computer science
Resume examples

Developed queries and performed extensive programming to access, transform, and prepare data for statistical modeling. Structured and translated requirements into an analytic approach.

• Conducted deep-dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision-making.
• Identified and diagnosed data inconsistencies and errors, documented data assumptions, and forages to fill data gaps.
• Engaged with internal stakeholders to understand and probe business processes to develop hypotheses.
• Guided test design, research design, and model validation.
• Served as the analytics expert and statistical consultant to cross-functional teams for large strategic initiatives and contributed to the growth of the company’s analytic community.
• Delivered insight presentations and action recommendations.
• Communicated complex analytical findings and implications to business.

Oversaw data management services including data modeling, database, and analytics platform design, database performance and optimization, recovery/load strategy, and implementation for the university.

• Led team of 5 in development and enhancements of the data user interface including data acquisition/access analysis. Managed vendor relationships
• Monitored status of assignments; reviewed code and documented scripts and procedures.
• Designed and implemented data verification and testing methods.
• Identified and evaluated opportunities to improve existing subject areas and applications and determine viability for adoption
• Provided technical expertise and direction in developing and supporting system-level programs Established standards and procedures related to end-user and interface development, including user requirements.
• Consulted with end-users, managers, and vendors to determine computing goals and system requirements.
• Worked with computer engineers and natural scientists to solve complex computing problems.

Worked in a Data and Systems Analysis (DSA) team collaborating with system engineers, data scientists, and machine learning specialists to develop fundamental product-facing and system-supporting architectures and tools to improve the surgeon experience, product adoption, and overall robotics architecture while connecting it to the hardware design.

• Built a machine learning framework by integrating machine learning libraries and operational frameworks into an end-to-end environment for data modeling, functional-space exploration, and guidance systems to improve patient outcomes, enhanced access and integration for surgeons and staff, and greater hospital efficiency.
• Engineered an ML pipeline to support development, experimentation, continuous integration, continuous delivery, verification/validation, and monitoring of AI/ML models.
• Collaborated with CE/RC/SI teams to improve the analysis efficacy and deliver actionable intelligence to improve procedure development and procedure adoption.
• Created documentation and best practices to share with the AI/ML community.

Explored the fundamental issues in computing and developed theories, models and processes to address those issues. Assisted lead data scientists and engineers to solve complex analytical problems. Played an integral role in developing new computing languages to improve the way users interact with information.

• Improved the efficacy of reporting and disseminating data findings to key decision-makers by revamping the data visualizing process.
• Researched and analyzed business needs and made recommendations to upgrade complex applications, system administration issues, and network concerns.
• Prepared and maintained technical data, reports, documentation, and briefings and created presentations to communicate information to various departments.
• Transformed user requests and requirements into technical design specifications and served as a liaison between all involved parties.
• Performed systems management and integration functions by reviewing system capabilities, workflow, and schedule limitations.

Explored problems and developed solutions and computing models to address situations utilizing tools to collaborate with engineers. Developed new programming languages to develop new software that improved financial processes within a multi-million dollar healthcare agency.

• Performed numerous tasks of varying scope and difficulty that were screened and reviewed by a higher-grade computer scientist or computer engineer.
• Planned work processes and procedures to select the appropriate data and best practices for assigned projects.
• Searched for information to identify and solve problems, recognizing situations that required referral to others.
• Led a small research team of technology engineers and architects in the emerging field of software-implementation fault tolerance for surgery processors.
• Created technical diagrams and drawings that described work to acquire patents for the technology.

Internalized business objectives and built models, action plans, and reporting frameworks to track progress. Analyzed and resolved business glitches by matching them with the most appropriate machine learning algorithm. 

• Leveraged cutting-edge statistical machine learning methods in analyzing wide-ranging data sets, building models that revolutionized the company’s financial forecasting.
• Modeled the performance of each department in the company, providing a basis for future planning.
• Ideated and developed sophisticated machine learning systems that enhanced the detection of malware, data breaches, and other fraudulent activity. 

Delivered ML project to customers from conception to completion by overseeing business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models with concept-drift monitoring and retraining. Utilized AWS AI services (Personalize), ML platforms (SageMaker), and frameworks MXNet, TensorFlow, PyTorch, SparkML, scikit-learn) to help customers build ML models.

• Researched and implemented novel ML approaches, including hardware optimizations on platforms such as AWS Inferentia.
• Worked with Professional Services consultants (Big Data, IoT, HPC) to analyze, extract, normalize, and label relevant data to operationalize customers’ models after they are prototyped.
• Wrote and delivered data-driven presentations about technical concepts to business, technical, and lay audiences.

Developed and implemented innovative IT solutions through modeling processes and made network improvements and reliability. Utilized a variety of new technologies for computer science research in collaboration with sponsors and business partners to receive additional development funding.

• Performed server technological upgrades to hardware and software, collaborating with computer scientists around the globe.
• Configured technical computing and network solutions for a variety of federal and state government agencies.
• Interfaced with clients to complete sales cycles and ensure that all requirements were fulfilled according to specifications and funding.
• Partnered with customers and program managers to deliver the highest value capabilities to improve performance along the way.
• Advised graduates on the development and conduction of capstone research projects in partnership with primary mentors.

Responsible for collecting data through various means to analyze business results and successes to set up and oversee new studies. Created experimental frameworks to collect data and streamline reporting processes to facilitate decision-making among key stakeholders.

• Collaborated with cross-functional teams to identify and prioritize areas of opportunity to drive user engagement and enhance experimentation activities.
• Assisted in the development of new analytic methods, function, and data architectures as necessary to complete complex projects.
• Developed analytics to qualitatively interpret end-user patterns and behavioral data that directly influence software design.
• Created models, algorithms, and mathematical models leading to meaningful insights that were visually presented to staff.
• Partnered with various data teams to make improvements to platform capabilities utilizing data modeling, experimentation, and data architecture.