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.

Determined computer and system requirement needs. Analyzed experimental results as directed by the senior computer scientist. Drafted, reviewed, and proofread reports and studies for publication. Implement innovative solutions to support lead scientists to problem solve complex situations.

• Supported senior computer scientist in generating research ideas and assisted in designing algorithms, software, and hardware.
• Interfaced with Information Technology Architects and Quality Assurance Leads in support of the senior computer scientist.
• Applied computer science principles and concepts in the research and development in the field of machine learning.
• Validated data and user acceptance testing (UAT), and scheduled and monitored processes for calculations.
• Collaborate with other departments and with all levels of management as directed by the senior computer scientist.

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.

Tasked with transferring data into new formats to make the information more appropriate for analysis. Built analytical tools to automate the data collection process and decrease the amount of time for reporting and suggesting technological advancements and/or changes.

• Created and improved computer software and hardware under the direct supervision of the lead data scientist and computer engineers.
• Helped develop streamlined algorithms to reduce the amount of processing time and make computer tasks more efficient.
• Tested scalable schema designs, relational database, query performance, workflow optimization, and documentation.
• Developed significant and concise analytic objectives according to business goals and initiatives and under the guidance of department supervisors.
• Designed and built interactive dashboards, machine learning models, and innovative analytics tools using a variety of programming languages.

Led 10-person team devising ingenious solutions to complex problems such as big data, business intelligence, and data analytics. Created partnerships with Cloudera, Datastax, and Ne04J.

• Patented three breakthrough methodologies that generated $7 million annually.
• Data Partitioning and Indexing (DPI): speeds up calculation and simplifies range-interval queries for data analytics.
• Compressed Data Warehouse (CDW): slashed server space, boosted efficiency, and accelerated querying.
• Automated Online Advertising System (OAS): applied game theory to calculate payments to inventory partners.
• Provided consulting services to C-Level executives for big data strategy, platform selection, reference architecture, ROI modeling, an enterprise data hub, real-time analytics, visualization, and migration to the cloud. Built a development team with combined subject-matter expertise in architecture and optimization for three areas of programmatic advertising:
• Advanced the state-of-the-art patented methodologies and algorithms —for automatic buying and selling of online ad impressions via real-time auctions that occur as fast as a webpage can load.
• Re-architected DMP and DSP platforms for a major ad-tech firm — converted to a real-time, in-memory platform.
• Built prescriptive architecture to run large production clusters for spot-market AWS instances.
• Architected cloud Infrastructure and massive data migration —from an on-premise data center to the public cloud— for the Washington Healthcare System.

Collaborated with product managers in outlining data analytics challenges and developing sophisticated algorithms for enhanced accuracy in predictions. Assisted with data collection, cleaning, and pre-processing.

• Revitalized the data analytics function by developing an innovative analysis system that accelerated the extraction of insights from large data sets.
• Worked closely with peers in the creation and implementation of new machine learning algorithms and predictive models.
• Created an algorithm to analyze employee productivity, leading to 98% success in employee management and task allocation.

Searched through large data sets for useful information to assist in the business decision-making process as directed by the lead data analyst and other key stakeholders. Demonstrated persistence, statistical, and engineering capabilities necessary in understanding biases and inconsistencies in data.

• Tasked to establish methodologies to improve algorithms to allow advancements in machine learning and cloud computing systems.
• Helped design new computer architectures to improve performance, effectiveness, and efficiency in computer software and hardware.
• Executed and troubleshot analysis workflow while maintaining excellent and comprehensive written records of activities.
• Collaborated effectively between the business department and data groups, by paying meticulous attention to detail and differentiating communication and presentation skills.
• Performed data exploration to understand end-user behavior and identify opportunities for improving software features.

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.

Leveraged machine learning and deep learning expertise in developing analytics solutions for pattern recognition in large data sets. Coordinated the design, development, and implementation of predictive models and forward-thinking algorithms which optimized value extraction from data sets.

• Led the data science team in developing and enhancing several models using problem resolution and process optimization algorithms.
• Conceptualized, developed, and rolled out innovative tools that improved the analysis of unstructured data.
• Directed numerous machine learning algorithm projects, writing tests to handle unforeseen errors effectively.