Senior AI Data Scientist – Solutions Developer

About the role

Senior AI Data Scientist – Solutions Developer

Location: San Antonio, TX / Plano, TX / Phoenix, AZ, US

Industry: Financial Services / Insurance / Technology

Work Setting: On-site, 4 days per week

Employment Type: Full-time

Salary: $143,320–$273,930 per year

Role Overview

Seeking a Senior AI Data Scientist / AI Data Solutions Scientist to develop advanced analytics, machine learning, generative AI, and agentic AI solutions that solve complex business problems and deliver measurable business value.

This role works cross-functionally with data engineering, software engineering, architecture, product, and business teams to transform structured and unstructured data into scalable, production-ready analytical solutions. The position combines traditional data science techniques such as statistical modelling, simulation, and optimization with LLMs, generative AI, RAG, and multi-agent systems.

Key Responsibilities

Gather, interpret, preprocess, and analyse structured and unstructured data.

Develop scalable and automated solutions using machine learning, simulation, optimization, and advanced analytics.

Select appropriate modelling techniques based on data limitations, business requirements, application needs, cost, latency, and reliability.

Develop, validate, document, and deploy models within Model Development Control (MDC) and Model Risk Management (MRM) frameworks.

Build and maintain reusable, production-quality algorithms, models, and supporting code.

Translate complex business requirements into analytical questions, models, and actionable recommendations.

Partner with business and analytics leaders to identify and prioritize high-value analytics and modelling initiatives.

Develop and deploy AI/ML and generative AI solutions in cloud environments.

Work with AI Engineers and Data Engineering teams to integrate models into production systems.

Develop solutions involving LLMs, agentic AI, RAG, prompt engineering, multi-agent systems, tool use, tuning, and observability.

Communicate complex analytical and modelling results to non-technical stakeholders.

Manage project milestones, risks, dependencies, and implementation challenges.

Establish best practices for production-ready analytical assets and model risk management.

Maintain awareness of emerging AI, machine learning, and data science technologies.

Mentor junior data scientists and contribute to data science communities and technical standards.

Ensure analytical activities comply with applicable risk, regulatory, and compliance requirements.

Required Qualifications

Bachelor’s degree in Mathematics, Computer Science, Statistics, Science, Engineering, AI, or another quantitative discipline, or equivalent relevant education and experience.

6+ years of experience in predictive analytics or data analysis, or an advanced quantitative degree with 4+ years of relevant experience.

4+ years of experience training and validating statistical, machine learning, physical, or other advanced analytics models.

4+ years of Python experience for statistical analysis and/or developing and scoring AI/ML models.

Strong experience with SQL, HQL, NoSQL, or other query languages and preprocessing data from structured and unstructured databases.

Strong understanding of descriptive, diagnostic, and inferential statistics.

Experience with statistical validation, model documentation, and model risk management.

Advanced knowledge of supervised machine learning, including regression, SVMs, decision trees, random forests, and related techniques.

Advanced knowledge of unsupervised machine learning, including k-means, hierarchical clustering, DBSCAN, and related algorithms.

Strong experience with LLMs and agentic AI systems, including frameworks such as LangChain, LangGraph, AgentCore, Vertex AI, MCP, or comparable technologies.

Experience with prompt engineering, model tuning/post-training, RAG, context optimization, multi-agent systems, tool use, and AI observability/monitoring.

Experience integrating MLOps practices and deploying production-scale AI solutions in AWS, GCP, or comparable cloud environments.

Ability to communicate technical findings and business recommendations to non-technical stakeholders.

Experience mentoring junior technical or data science professionals.

Preferred Qualifications

Experience in financial services, insurance, banking, or another highly regulated industry.

Experience with cloud-native application development and modernization.

Military experience or experience as a military spouse/domestic partner.