Thomas Loeber Email & Phone Number

Glen Allen, VA, US

Chicago, Illinois, US

Successfully productionized a RAG proof of concept, which proved crucial to handle increased customer demand during the annual Medicare enrollment period, as it enabled call center agents to compare different Medicare plans by asking questions in natural language, instead of searching hundreds of pages of coverage documents.

Seattle, WA, US

• Helped clients productionize their ML products by adopting MLOps practices. Worked with a large utility company to leverage ML & AI to better predict and mitigate wildfire risk.
• Leveraged the AWS SageMaker platform to streamline model management, deployment, monitoring, and scaling.
• Built feature pipelines using PySpark, AWS Glue, Athena, and Step Functions.
• Managed ML infrastructure as code using Terraform.Contributed to the consulting firm’s internal initiatives for continuous improvement:
• Created reusable templates for MLOps projects to enable easy adoption by new use cases, while at the same time building in best practices.
• Served on the GenAI committee, aimed at driving the adoption of Generative AI throughout the organization by providing training and resources.

Palo Alto, California, US

• Assisted a Y Combinator-backed GenAI startup, a leader in synthetic speech generation, in integrating engineering best practices with Machine Learning to transform innovative generative AI research into robust software.
• Deployed and maintained Pytorch LLMs for speech generation.
• Introduced MLOps and DevOps practices to reduce waste as well as to address reliability and scalability issues stemming from manual workflows.
• Containerized ML applications.
• Automated infrastructure management using Ansible.
• Built a CI/CD pipeline using GitHub Actions.

Mclean, VA, US

• Contributed to two projects aiming to build a state-of-the-art data infrastructure using AWS and cloud-native tooling:Joining a team that was about to enter production, took the lead in shifting the focus to.
• Led the adoption of automated unit and acceptance testing.
• Enabled local development of AWS resources by making it easy to spin up required infrastructure using docker-compose, allowing for much quicker iteration.Moved to a critical greenfield project to design and build a.
• As part of a small team, researched different frameworks, tools, and architectures. Contributed to successful proposal centered around reactive microservices built on the Kubernetes ecosystem.
• Designed and implemented transformation flows using Scala and Kafka.
• Built gRPC APIs for communication between microservices.

Glen Allen, VA, US

Glen Allen, VA, US

• Took on an expanded role as a Data Analyst, Data Scientist, and Data Engineer with team, project, and organizational responsibilities. Planning and preparing for a large-scale modernization effort to transform a.
• MACHINE LEARNING - Evaluated approaches to leverage machine learning and time series methods to improve demand forecasting in a global supply chain with long lead times. Centrally involved in building prototypes of.
• ENGINEERING EXCELLENCE - Implemented practices for automated testing (using pytest), frequent refactoring, proper commenting (including self-documenting code), coding standards (PEP-8), version control (Git), and.
• CLOUD ADOPTION – Together with my manager, pioneered the use of AWS at the company and demonstrated the value of the cloud across diverse use cases. As the entire company later migrated from on-prem to Azure, served at.
• SPARK - Created a data pipeline using Databricks/PySpark. Fed data of an Alexa-enabled IoT product from Amazon owned S3 buckets into an IoT engineering dashboard, to enable monitoring performance data of IoT devices.
• DEVOPS - Built a continuous integration pipeline using Azure Pipelines.

Chapel Hill, NC, US

• Acquired a rigorous foundation in statistical analysis, R programming, research design, and machine learning using Python, Scikit-Learn, and Pandas.
• Conducted research to address long-standing methodological challenges in the social sciences. In particular, related to causal inference with non-experimental data, the replication crisis, and modeling complex causality.
• As part of a self-study program during the last 2 years, used machine learning methods and wider insights from the data science process (in particular, regarding model validation) to address the shortcomings of the.
• Served as a Teaching Assistant for political science courses during four semesters, communicating complex ideas to undergraduate students in a simple and understandable format.
• Eventually left program after completing required coursework for PhD, because I realized industry is a better fit for me than academia.

Salzburg, Österreich, AT

• Performed exploratory data analysis and hypothesis testing using SPSS for a medical research project to investigate the efficacy of pain management in hospitals, nursing homes, and hospices.
• Prepared reports that incorporated data visualization to communicate findings.

Ma, Political Science

Data Science Career Track

Visiting Graduate Student

Bachelor Of Arts (B.A.), Sociology

Bachelor Of Science (Bsc), Economics