Data analyst

Data analyst
Resume examples

Resume Examples Objectives and summaries
Data analyst
Data analyst
Resume examples
Resume Examples Objectives and summaries

Data engineer

Experienced

Wrote the most complex ETL (Extract / Transform / Load) processes, designs database systems, and developed tools for real-time and offline analytic processing. Troubleshoot software and processes for data consistency and integrity. Led the integration of highly complex and large-scale data from a variety of sources for business partners to generate insight and make decisions.

  • Translated business specifications into design specifications and code.
  • Established analytical rigor and methods for writing complex programs, ad hoc queries, and reports; ensured all code is structured and documented and is easy to maintain and reuse.
  • Partnered with internal clients and leaders to gain an expert understanding of highly strategic, high-risk business functions and informational needs.
  • Worked closely with technical and data analytics experts across the business to implement data solutions.
  • Acted as the highest point of escalation for data analysis and served as a technical consultant for the client.
  • Created an analytics-driven environment by gathering requirements, assessing gaps, and building roadmaps and architectures.
  • Educated and developed junior data engineers on the team while applying quality control to their work and increasing their knowledge in specialized Data Engineering techniques and processes.
  • Tested and implemented highly complex new software releases through regression testing. Identified issues and engaged with vendors to resolve and elevate software into production.
  • Turned raw data into usable data pipelines and built data tools and products for effort automation and easy data accessibility.
  • Diagnosed the existing architecture, data maturity, and identify gaps.
  • Built data assets that enhance the quality of overall data structures by implementing the data science program.
  • Identified gaps and implement solutions for data security, quality, and automation of processes.

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Data scientist

Experienced

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

Experienced

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 analyst

Entry level

Assess key parameters using SQL and Python to drive product development with insightful analysis and collaboration. Maintain dashboards and data warehouses to quickly deliver information. Evaluate existing practices for efficacy and accuracy.

  • Optimize existing Tableau SQL queries to process data for new product launches.
  • Increase product sales 12% via market and exploratory analyses.
  • Improve SPSS syntax execution by debugging outdated code and writing Python scripts to automate processes for integration with other applications.

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Data analyst

Experienced

Develop insights and data visualizations to maximize opportunity and increase efficiency. Prepare monthly reports for management and key stakeholders. Translate complex data models to understandable written narratives for ad hoc information requests.

  • Lead a junior data analyst team to synthesize data driven reports and present findings interdepartmentally.
  • Optimize Tableau dashboard systems through outdated query removal and REGEX string implementation.
  • Develop and standardize data cleaning procedures to validate accuracy using both Microsoft Excel and Tableau Prep.

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Data analyst

Senior

Discover novel insights using SQL and Python databases. Collaborate with project management and sales to create effective strategies addressing client needs and product development. Prepare and present annual revenue forecasts to C-suite managers and key stakeholders. Conduct financial and market research to produce automated tools and support company initiatives.

  • Spearhead data flow improvements through high-performance Pandas queries to organize data from 4.2K customers.
  • Incorporate SPSS key performance analyses into existing departmental framework to decrease costs.
  • Boosted campaign engagement 29% by providing digital evaluations to marketing departments.
  • Raised sales 14% by conducting focus group research for underperforming products.

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