Senior Data & AI Scientist - GenAI & Data Strategy

Laudio; Leawood (KS); Hybrid; USD 151,000 - 216,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

Company paid life insurance

401(k) with company match

Paid time off and parental leave

Employee assistance program

Headspace and Airvet wellness programs

Charitable matching program

Job summary

Ascend Learning is seeking a Senior Data and AI Scientist to lead strategic AI initiatives and advance data readiness for enterprise AI/ML use cases. You will design generative AI tools and scalable data science solutions spanning multiple business functions.

The role requires hands-on experience with ML models, LLM tooling, and collaboration with Engineering, Data, and Operations teams to embed AI capabilities in products and workflows.

Qualifications

• Master's degree in Statistics, Data Science, Computer Science, Mathematics, Operations Research, or a similar quantitative field.
• A minimum of 7 years' experience on data science, ML, and AI projects in industry, with emphasis on applied AI/generative AI/LLM solutions.

Responsibilities

• Lead strategic data science and AI initiatives across Ascend Learning.
• Design and implement advanced analytical and AI/LLM-based solutions across functions.
• Build LLM-based tools and interfaces for self-service data access.
• Develop and refresh ML/AI models with reproducible engineering.

Skills

Data science stack

Python tools

Generative AI

LLM tooling

Snowflake metadata

Relational databases

NoSQL databases

Data visualization

Education

Master's degree in Statistics/Data Science/CS/Math

Tools

Python (NumPy/SciPy/Scikit-learn)

TensorFlow

Spark ML

Azure ML

Snowflake

Job description

Ascend Learning is seeking a Senior Data and AI Scientist to lead strategic AI initiatives and advance data readiness for enterprise AI/ML use cases. You will design generative AI tools and scalable data science solutions spanning multiple business functions.

The role requires hands-on experience with ML models, LLM tooling, and collaboration with Engineering, Data, and Operations teams to embed AI capabilities in products and workflows.