Data Scientist II

SWBC•San Antonio, TX

About The Position

SWBC is seeking a talented individual who will contribute to the development of machine learning models, analytical solutions, and AI-driven capabilities that directly impact business performance and client outcomes. This role works closely with the Senior Data Scientist, Analytics Engineers, and Data Analysts to design, build, and deploy data science solutions within SWBC's modern, cloud-native data ecosystem and enterprise AI/ML platform. The Data Scientist II is expected to independently lead analyses and model development projects with minimal guidance, while contributing to the broader AI strategy of the organization. In this role, you will work hands-on with cutting-edge tools and platforms every day, including Hex for experimentation, Omni for AI context development powering Clara, and SWBC Intelligence our multi-model AI orchestration platform spanning AWS Bedrock and AWS Sagemaker to build and deploy intelligent solutions for internal and client-facing use cases. We offer a collaborative environment that values continuous learning, empirical rigor, and professional growth.

Requirements

Master's degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or a related discipline. Equivalent professional experience (5+ years) may be considered in lieu of an advanced degree.
Minimum of two (2) years of progressive experience in data science, analytics, or machine learning roles.
Proficiency in Python and SQL, with working knowledge of ML frameworks such as TensorFlow, PyTorch, scikit-learn, or equivalent.
Experience developing and deploying models within cloud-based AI/ML platforms, preferably AWS (SageMaker, S3) and Snowflake.
Familiarity with MLOps tools and practices, including MLflow, experiment tracking, and model versioning.
Proficiency in Hex for data science experimentation and prototyping.
Understanding of statistical modeling, hypothesis testing, and experimental design.
Ability to work with structured and semi-structured data, perform data cleaning, and conduct exploratory analysis.
Strong communication skills with the ability to present findings to both technical and non-technical audiences.
Ability to interpret ambiguity and work with minimal direction on defined project scopes.
Ability to lift 20 lbs. of files, supplies, documents, or other related items.

Responsibilities

Design, develop, and deploy machine learning models and analytical solutions to address business problems, including forecasting, segmentation, classification, and anomaly detection.
Contribute to the development and enhancement of Clara, SWBC's AI decision assistant, including supporting context engineering within Omni, prompt development, and model evaluation workflows.
Build and maintain ML/AI experiment workflows using Hex for prototyping and exploration, and AWS SageMaker and MLflow for experiment tracking, feature engineering, and model versioning.
Develop and deploy AI/ML solutions within SWBC Intelligence, the enterprise multi-model AI orchestration platform, leveraging AWS Bedrock and AWS Sagemaker as directed by senior team members.
Conduct exploratory data analysis and feature engineering using data sourced from SWBC's governed medallion architecture (Bronze - Silver - Gold) in Snowflake.
Participate in the platform's Evaluation Harness process, including test case development, model benchmarking, and scoring framework contribution to ensure solution quality.
Perform AI experimentation and prototyping within governed sandbox environments, including Snowflake Sandbox and Hex, ensuring adherence to data privacy and compliance requirements.
Apply Responsible AI principles, including bias detection, model explainability, and fairness monitoring, to all model development activities.
Collaborate with cross-functional teams including Analytics Engineering, Data Management, and business stakeholders to translate business problems into data science solutions.
Communicate findings, model results, and recommendations to both technical and non-technical audiences through clear documentation, visualizations, and presentations.
Stay current with emerging trends in data science, machine learning, and AI, and contribute to the team's knowledge sharing and continuous improvement culture.