22Data analyst resume examples found
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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.
Developed queries and performed extensive programming to access, transform, and prepare data for statistical modeling. Structured and translated requirements into an analytic approach.
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.
Collaborated with three junior data analysts to process usable information and reported findings to appropriate managers and decision-makers. Helped to establish KPIs to evaluate the effectiveness of the company’s financial decisions, requiring the analysis of large data sets to find relevant information.
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.
Saved enterprise technology company $900K+ in expenses over 1-year timeframe via migration initiative. Oversaw project planning, database integration, and engineering operations as project leader.
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.
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.
Automated data ingestion procedures and improved overall reporting efficiency by 40% with the introduction of custom-built data views and reports. Collaborated with stakeholders and supervisor to define requirements and deliver tailored solution.
Worked with senior data analysts to identify areas and opportunities for business improvement. Collaborated with junior analysts to collect and analyze data. Created and reviewed reports for internal/external stakeholders under the supervision of the senior data analyst.
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