21Computer science resume examples found
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Developed queries and performed extensive programming to access, transform, and prepare data for statistical modeling. Structured and translated requirements into an analytic approach.
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.
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.
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.
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.
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.
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.
Oversaw data management services including data modeling, database, and analytics platform design, database performance and optimization, recovery/load strategy, and implementation for the university.
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.
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.
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