Data scientist
Resume examples

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.

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.

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.

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.