Sr Data & AI Scientist

Ascend Learning, LLC•Leawood, OR

2d•$151,000 - $215,930•Hybrid

About The Position

The Senior Data and AI Scientist leads strategic data science and AI initiatives across Ascend Learning, driving AI adoption and improving enterprise data readiness for AI and machine learning use cases. This role designs and implements advanced analytical and AI/LLM-based solutions - including generative AI and conversational data-access tools - that span multiple business functions, translating complex business needs into scalable, high-value data science and AI solutions.

Requirements

Master's degree in Statistics, Data Science, Computer Science, Mathematics, Operations Research, or a similar quantitative field.
A minimum of 7 years' experience working on data science, machine learning, and artificial intelligence projects in industry, with a strong and growing emphasis on applied AI and generative AI/large language model-based solutions.
Deep, hands-on technical expertise across the full data science and applied AI stack.
Advanced proficiency with data science toolkits (e.g., Python NumPy, SciPy, Scikit-learn, TensorFlow, R, Spark ML, Azure ML), programming and scripting for reproducible analysis, and building classification, regression, clustering, and deep learning models.
Specialized knowledge of generative AI and large language model tooling and frameworks (e.g., retrieval-augmented generation, prompt engineering, vector embeddings), and hands-on experience defining semantic models and metadata within Snowflake so enterprise data is reliably consumable by both business users and AI systems.
Strong applied statistics skills, experience with both relational and NoSQL databases, and familiarity with data visualization tools.
Judgment to translate ambiguous business requirements into well-defined, high-value solutions.
Ability to do minimal travel, less than 10%.

Responsibilities

Implement enterprise data AI strategy to drive data-readiness, and enable AI adoption: identify and evaluate opportunities to apply artificial intelligence and generative AI data analytics across the organization, and assess and improve the quality, structure, and governance of enterprise data so it is fit for AI and machine learning use.
Design and build large language model-based tools and interfaces (e.g., Claude) that let business users and internal platforms query and act on organizational data using natural language, increasing AI adoption and self-service access to data-driven insights.
Design, build, and refresh machine learning and AI models: conduct experiments and proof-of-concept research, prepare data, and develop the underlying data models and databases needed to support new and existing business initiatives, applying disciplined engineering practices such as version control, automated testing, and documentation to ensure reliable, reproducible solutions.
Convert business questions into analytical solutions: translate business needs into clearly defined analytics problem statements, interpret results using techniques ranging from simple data aggregation to complex data mining, and apply data science methods to support functions such as sales targeting, so business partners can make informed, data-driven decisions.
Partner with leadership and technical teams across the organization: collaborate with Engineering, Data, and Operations teams to embed data science and AI capabilities into products and workflows, and work directly with business leaders and end-users from concept through delivery to understand business trends, needs, and problems.