Data scientist

Data scientist
Resume examples

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Data scientist
Data scientist
Resume examples

9Data scientist resume examples found

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Data scientist


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.

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Data scientist

Entry level

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.

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Data scientist

Entry level

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.

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Data scientist


Evaluated technology requirements to accurately and efficiently analyze data, revamped out-of-date systems, and reduced technology costs throughout the company. Developed and supported multiple databases and provided high-level technical support to clients.

  • Managed the development team, introduced project management best practices, and established formal project planning and scoping processes
  • Improved analytical accuracy from 92% to 97% within 6 months through rigorous development and implementation of new processes and procedures.
  • Saved the company $300K by designing and implementing in-house data analytics governance, change management, and training scheduling systems.
  • Optimized resources by outsourcing data backup and initiating the company’s first off-site backup for data.
  • Co-introduced and championed improved methods for data visualization as a cost-effective solution to present findings to clients.

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Data scientist


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.

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Data scientist


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.

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Data scientist


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.

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Data scientist


Developed and implemented various software systems to establish the basis of modern UX and tested their operation to ensure company initiatives were met. Helped draft, edit, and proofread findings and reports for publication in academic journals and magazines and co-led conference presentations.

  • Developed and deployed programs and packages in Python that were used to effectively process data, generate reports, and automate workflow.
  • Created and implemented user modeling in intelligent systems such as search, recommendation, and conversational.
  • Interrogated data to solve previously identified business problems and then presented recommendations to department heads and other key stakeholders.
  • Applied innovative machine learning and statistical analysis processes to create models related to opportunity sizing and target optimization.
  • Improved or developed the practice of offline and online A/B experiments and analyzed data to present findings to department heads.

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