Senior Data Scientist/AI Application Solutions Engineer

VSP Vision Care•Cynthian, OH

1d•$84,000 - $141,750

About The Position

The Senior Data Scientist/AI Application Solutions Engineer models complex business problems and discovers business insights using statistical, algorithmic, data-mining, and visualization techniques. The role conducts experiments and develops methodologies to generate and collect data for business use, mines available data for trends and patterns, and shares insights with lines of business and senior leadership. The role partners with business stakeholders to identify analytical questions and experiments, performs complex statistical analyses, and develops predictive models, algorithms, and probability engines. It evaluates models against real-world results and communicates findings to product, service, and business managers. It also develops software, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from disparate sources. The position applies emerging data science, machine learning, deep learning, and artificial intelligence techniques to business decisions and data-driven capabilities. It supports user experience specialists and information architects with dashboards and visualization practices, consults with analytics and product teams on data-based experiments, and helps communicate enterprise data governance principles. Preferred experience includes developing and deploying enterprise AI, generative AI, and analytics solutions across their lifecycle, including requirements, design, development, testing, implementation, and adoption. The role may work with LLMs, intelligent agents, RAG, vector embeddings and databases, semantic search, Snowflake Cortex, cloud data and AI platforms, and modern application technologies. Solutions should be scalable, secure, maintainable, and evaluated for accuracy, reliability, performance, and business value. The role also calls for attention to monitoring, observability, operational support, security, privacy, governance, responsible AI, and compliance.

Requirements

Bachelor’s degree in computer science, data science, statistics, economics, or a related functional area, or equivalent experience.
Six years of demonstrated experience in analytics, data science, machine learning, AI platform or solution development, data and AI engineering, cloud platform engineering, or a related discipline.
Flexibility to change priorities and direction quickly and effectively.
Excellent written and verbal communication, presentation, and analytical skills.
Strategic skills, business acumen, and curiosity to support envisioning work, program planning, and strategic partnerships.
Ability to regularly exercise discretion and independent judgment in performing job duties.
Ability to remain current in the field, including new techniques, approaches, and software.
Strong collaboration and communication skills and ability to translate business needs into technology solutions. (Preferred Skills)

Nice To Haves

Experience developing and deploying AI, generative AI, and analytics solutions for enterprise knowledge discovery, automation, and decision support.
Experience partnering across the full solution lifecycle, including requirements gathering, design, development, testing, implementation, and end-user adoption.
Demonstrated ability to deliver scalable, secure, and maintainable solutions using generative AI, intelligent agents, and advanced analytics technologies.
Proficiency with modern application development technologies such as Python, React, TypeScript, Node.js, APIs, and data visualization frameworks.
Experience designing and implementing AI-enabled solutions using large language models, machine learning capabilities, vector embeddings, semantic search, Snowflake Cortex, and other emerging AI technologies.
Experience building and deploying Retrieval-Augmented Generation solutions, including document ingestion, data chunking, embeddings, vector databases, retrieval strategies, prompt engineering, and response generation.
Ability to evaluate and optimize AI-generated outputs for accuracy, reliability, performance, security, and business value.
Experience using AI-assisted development tools and modern engineering practices while maintaining code quality, testing, security, and maintainability.
Knowledge of performance monitoring, observability, reliability, scalability, and operational support for AI and analytics solutions in production environments.
Understanding of security, privacy, governance, responsible AI principles, and compliance considerations related to AI and analytics development.
Experience working with cloud-based AI, analytics, and data platforms in enterprise environments.

Responsibilities

Model complex business problems and discover business insights using statistical, algorithmic, mining, and visualization techniques.
Conduct experiments and develop methodologies to generate and collect data for business use.
Mine available data to identify trends and patterns and generate insights for lines of business and senior leadership.
Partner with lines of business to identify questions and issues for data analysis and experiments.
Perform complex statistical analysis on experimental or business data to validate and quantify trends or patterns.
Construct predictive models, algorithms, and probability engines to support data analysis or product functions, and verify their effectiveness against real-world results.
Identify meaningful insights from large data and metadata sources and communicate findings to product, service, and business managers.
Develop and code software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from disparate sources.
Research and apply existing and emerging data science principles, theories, and techniques to inform business decisions.
Use advanced mathematical and statistical concepts to analyze and collect data and construct solutions to business problems.
Participate in research and implement machine learning, deep learning, and artificial intelligence techniques and tools to improve data analysis and create data-driven capabilities and insights.
Support user experience specialists and information architects in enhancing information visualization, including dashboards and user interface best practices.
Consult with analytics and product teams on data-based experimentation.
Assist in communicating enterprise data governance principles throughout the company.
For preferred solutions work, partner across requirements gathering, design, development, testing, implementation, and end-user adoption.
Evaluate and optimize AI-generated outputs for accuracy, reliability, performance, security, and business value.
Support performance monitoring, observability, reliability, scalability, and operational support for production AI and analytics solutions.