Senior Data Scientist

SWBC is seeking a talented individual who will lead the development of our most critical and complex machine learning and AI initiatives. This role is a technical leadership position responsible for setting the strategic direction for data science projects, mentoring the Data Science team, and ensuring our models are scalable, reliable, and directly tied to business outcomes. The Senior Data Scientist will serve as a subject matter expert and strategic partner to business and executive leadership, driving the development and deployment of intelligent solutions across SWBC's enterprise AI/ML platform. Why you'll love this role: In this role, you will lead the design and delivery of intelligent solutions that power Clara, SWBC's AI decision assistant, and drive enterprise-wide analytics through forecasting models, segmentation analysis, and context engineering for AI self-service. Your daily work will center on developing and deploying within SWBC Intelligence, our multi-model AI orchestration platform spanning AWS Bedrock, AWS Sagemaker, alongside Hex for experimentation and Omni for AI context development powering Clara and SWBC Insights. Essential duties include the following: Act as the technical lead for high-impact data science projects, from ideation through production deployment, ensuring alignment with business objectives and compliance standards. Design and implement machine learning and AI solutions, including predictive models, forecasting frameworks, segmentation analysis, and natural language processing capabilities. Lead the development and enhancement of Clara, SWBC's AI decision assistant, including context engineering within Omni, model evaluation, and continuous intelligence improvement. Develop and deploy AI/ML solutions within SWBC Intelligence, the enterprise multi-model AI orchestration platform, leveraging AWS Bedrock and AWS Sagemaker for internal and client-facing use cases. Build and maintain AI/ML experiment workflows using Hex for prototyping and exploration, and AWS SageMaker and MLflow for experiment tracking, model versioning, and lifecycle management within the platform. Contribute to the platform's Evaluation Harness process, including golden dataset curation, rubric-based scoring, adversarial testing, and model performance benchmarking to ensure solution quality before production deployment. Conduct AI experimentation and prototyping within governed sandbox environments, including Snowflake Sandbox and Hex, while adhering to data privacy, masking, and compliance requirements. Collaborate with Analytics Engineers and Data Management teams to source model training data from governed medallion layers (Bronze → Silver → Gold), ensuring data quality and lineage traceability. Champion Responsible AI practices, including explainability, bias monitoring, fairness metrics, and model auditability in alignment with SWBC's governance framework. Lead model validation, A/B testing, and performance monitoring to ensure models meet business requirements and maintain production-grade reliability. Partner with IT platform engineering teams to provide feedback on platform capabilities, identify gaps, and advocate for enhancements that improve the Data Science team's development and deployment experience. Mentor, coach, and provide technical guidance to the Data Science team, fostering a culture of continuous learning, empirical rigor, and innovation. Publish technical documentation and best practices to advance the team's capabilities and contribute to the organization's knowledge base. Serious candidates will possess the minimum qualifications: Master's or Ph.D. in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or a related discipline. Equivalent professional experience (8+ years) may be considered in lieu of an advanced degree. Minimum five (5) years of progressive experience in data science, with a portfolio of deployed models that have driven significant business value. Extensive experience with advanced machine learning techniques, including deep learning, natural language processing (NLP), time series forecasting, or computer vision. Proficiency in Python and strong understanding of ML frameworks such as TensorFlow, PyTorch, scikit-learn, or equivalent. Hands-on experience developing and deploying models within cloud-based AI/ML platforms, including AWS SageMaker, Bedrock, and S3. Experience with MLOps tools and practices, including MLflow, model registries, CI/CD for ML pipelines, and automated evaluation frameworks. Proficiency in Hex for data science experimentation and prototyping, and familiarity with Omni for analytics and AI context development. Familiarity with Snowflake for data sourcing and analytics within a governed medallion architecture. Strong understanding of data governance, compliance, and responsible AI principles within regulated industries. Financial services or insurance experience preferred. Exceptional communication and leadership skills, with a track record of influencing strategic decisions and presenting complex findings to non-technical stakeholders. Demonstrated ability to work autonomously, manage projects, and make key decisions with minimal guidance. Ability to lift 20 lbs. of files, supplies, documents, or other related items. SWBC offers*: Competitive overall compensation package Work/Life balance Employee engagement activities and recognition awards Years of Service awards Career enhancement and growth opportunities Leadership Academy and Mentor Program Continuing education and career certifications Variety of healthcare coverage options Traditional and Roth 401(k) retirement plans Lucrative Wellness Program *Based upon employee eligibility Additional Information: SWBC is a Substance-Free Workplace and requires pre-employment drug testing. Please note, SWBC does not hire tobacco users as allowed by law. To learn more about SWBC, visit our website at . If interested, please click the appropriate apply button.