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About This Role
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 www.SWBC.com. If interested, please click the appropriate apply button.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At SWBC, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
SWBC AI Hiring
SWBC has 1 open AI role right now. They're hiring across Data Scientist. Based in San Antonio, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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