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It would take months to narrow them down for interviews, let alone figure out who to hire. Now, imagine a hiring manager going through all of them one by one. Large companies usually get hundreds of resumes for each open position they announce.

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To make sure that your resume can be read by an ATS, we’re going to provide 16 free ATS-friendly resume templates!īut first, let’s start with: What’s an ATS-Friendly Resume Template? Sometimes, though, an ATS rejects your resume just because it can’t read your resume template because of formatting issues.

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Your resume could have been rejected by an applicant tracking system (ATS in short).Īn ATS is a type of software that automatically scans your resume and determines whether or not it’s relevant for the position. You might chalk that up to “I just wasn’t a good fit,” but sometimes, the reason for it is something else entirely:

Worked with computer engineers and natural scientists to solve complex computing problems.Have you ever put so much effort into a job application, only to never get a call-back from a recruiter?.

Consulted with end-users, managers, and vendors to determine computing goals and system requirements.

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.

Identified and evaluated opportunities to improve existing subject areas and applications and determine viability for adoption.

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Designed and implemented data verification and testing methods.

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Monitored status of assignments reviewed code and documented scripts and procedures.

Led team of 5 in development and enhancements of the data user interface including data acquisition/access analysis.

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

Architected cloud Infrastructure and massive data migration -from an on-premise data center to the public cloud- for the Washington Healthcare System.

Built prescriptive architecture to run large production clusters for spot-market AWS instances.

Re-architected DMP and DSP platforms for a major ad-tech firm - converted to a real-time, in-memory platform.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.Built a development team with combined subject-matter expertise in architecture and optimization for three areas of programmatic advertising: 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.Automated Online Advertising System (OAS): applied game theory to calculate payments to inventory partners.Compressed Data Warehouse (CDW): slashed server space, boosted efficiency, and accelerated querying.Data Partitioning and Indexing (DPI): speeds up calculation and simplifies range-interval queries for data analytics.Patented three breakthrough methodologies that generated $7 million annually.Created partnerships with Cloudera, Datastax, and Ne04J. Led 10-person team devising ingenious solutions to complex problems such as big data, business intelligence, and data analytics. Created documentation and best practices to share with the AI/ML community.Collaborated with CE/RC/SI teams to improve the analysis efficacy and deliver actionable intelligence to improve procedure development and procedure adoption.Engineered an ML pipeline to support development, experimentation, continuous integration, continuous delivery, verification/validation, and monitoring of AI/ML models.

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.

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.