Tom Kelm

About

Tom has extensive experience in Data Engineering, Data Science, and Data Ops. His career has focused on helping clients leverage data to drive innovation and business success. Notable skills include Python, Spark, and Java, with expertise in Infrastructure as Code and Workflow Orchestration.

Experience

• Product Director of Data · Legends2023-05-01 –

  - Provide technical guidance to CTO and CPO all things related to data - Work with customers to define requirements and follow through with the build process for a given solution - Oversee onboarding of tier 1 sports teams onto our eCommerce platform (B2C and B2B) - Serve as the liaison between business and technical resources - Stood up a reporting platform using open source technologies - Understand, research, and follow technical trends in the industry and assess new products on the market - Drive build vs buy conversations for product feature requests

• Solutions Architect · Amazon2022-07-01 – 2023-05-01

  - Engage customers via collaboration with ad tech sales managers and sales executives to develop strong customer relationships, vet requirements upfront, and drive excitement for the right ad tech solution that achieves the customer’s business outcomes - Support a platform to deliver analytics in the Ad Tech space from Amazon Marketing Cloud into a customers AWS Account - Built an accelerator to automatically generate insights from Creatives leveraging AWS's suite of image and text analysis tools - Develop software code /scripts and / or build automation of analytics / dashboards to create custom solutions / insights for business outcomes - Drive usage and adoption of Amazon Advertising Technologies to activate advertiser’s campaign and marketing insights. You will continuously monitor the integration inputs you drive to measure the output of activation - Serve as technical oversight simultaneously for multiple engagements across customers - Create trainings for Ad Tech consultants who were the delivery resources on projects

• Architect - Data & Analytics · Credera2022-01-01 – 2022-07-01

  - Architected data pipelines for a large airline company in AWS using various AWS Services (Glue, Lambda, Step Functions, Event Bridge, SQS, etc.) to send data to a third party vendor for a new engine optimization initiative forecasted to save $14 million over the next 5 years - Led the development team for the data pipelines and provided regular feedback to continually improve all team members abilities - Developed the Cloud Formation Infrastructure as Code and GitLab CI/CD pipelines that would be used to deploy our solutions - Owned the promotion of our solution through the Dev, QA, and Production environments by coordinating with several teams (Data Lake, Cyber Security, and Business Owners) - Facilitated an AWS account migration of our solution by coordinating with cross functional teams who owned various components - Built Confluent Cloud Kafka reference implementations in Python and Java on AWS (Lambda and ECS w/ Fargate) for a large airline company using best practices - Member of Credera's MLOps go to market team, which organizes Credera's thoughts and best practices for MLOps into tangible artifacts (reference architectures, blogs, whitepapers, etc.) that can be used to help during business development cycles with clients

• Senior Consultant - Data & Analytics · Credera2020-01-01 – 2022-01-01

  - Leader of Credera's Data Science Special Interest Group (SIG), which holds monthly meetings discussing major breakthroughs in Machine Learning and organizing SIG Projects to give our consultant's project like experience in various ML areas (ranging from Kaggle-like competitions to specific requests that we had heard through our clients that we re-created with open source data) - Expanded a real-time stream-processing platform (Python / Kafka / Kubernetes) for a financial services company to be more scalable and fault tolerant via a custom Kubernetes Operator and expanding Kubernetes autoscaling functionality to abstract away Kubernetes from Data Scientists and reduce operational costs of the plaftorm - Built a Data Lake solution for a financial services company leveraging Kubernetes, Kafka, Debezium, and AWS Data Lake tools ( S3, Glue, Athena, Iam, etc.) that enabled self service BI throughout the enterprise - Integrated MLOps best practices for a financial services company to automatically re-train models and deploy them using GitLab CI/CD pipelines; required standing up Gitlab Runners that were able to leverage GPU acceleration to retrain models

• Consultant - Data & Analytics · Credera2018-01-01 – 2019-12-01

  - Developed KPI's and Power BI Dashboard for a professional services C-suite to assess the adoption and success of their recently rolled out mobile application - Implemented a Data Lake solution for a health insurance claims analysis company leveraging Actian Vector and HDFS that was used to process TB of data to find incorrectly coded entries that could be audited to save money - Built a Machine Learning Proof of Concept for a financial services company that would classify documents and extract keywords off of an image (Invoice number, amount, etc.) to improve the speed of payment to invoice factoring clients - Architected a real-time stream-processing platform (Python / Kafka / Kubernetes) for a financial services company that would allow them to build complicated workflows to solve business process with Machine Learning

• Citi Architecture and Technology Engineering Summer Analyst · Citi2017-05-01 – 2017-08-01

  Implemented an ARIMA time series forecasting model for server utilization that would be used to forecast out required capacity 6+ months in advance (lead time to provision new servers in private cloud)

• Disaster Recovery Intern · Rentsys Recovery Services2016-05-01 – 2016-08-01
• Research Assistant · Texas A&M University2014-05-01 – 2015-01-01
• Intern · Kelm Engineering, LLC2011-06-01 – 2013-08-01

Education

• Texas A&M UniversityBachelor of Science (BS), Computer Science

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