What are the responsibilities and job description for the Staff AI Data Scientist (Remote) position at KOHLS?
About the Role
As Staff AI Data Scientist, you will work with a data science team and cross-functional partners to solve business challenges and promote data-driven decision-making with advanced data analysis and machine learning.
What You’ll Do
- Use data and insights to make decisions, set goals and identify the right levers to achieve balanced team objectives
- Lead end-to-end data science projects from problem formulation to model deployment
- Oversee the design of experiments that answer targeted questions
- Identify and drive continuous improvement of key business metrics in the balanced team
- Translate data science outputs into business outcomes and value delivered
- Maintain strong business partner relationships to gain cross-organizational alignment, spur adoption and usage of data science capabilities and drive business outcomes
- Mentor and guide junior data scientists, providing technical expertise and fostering a culture of continuous learning and development
- Stay up to date on the latest trends and developments in data science and technology and identify implementation opportunities to support innovation at Kohl’s
- Additional tasks may be assigned
Addendum
STAFF AI DATA SCIENTIST
- Develop prescriptive AI/ML solutions - including foundation models and predictive systems - by converting complex business constraints into mathematical objective functions that guide optimal agent behavior.
- Build and enforce continuous, quantitative evaluation harnesses to benchmark reasoning quality, tool-calling precision, grounding, and hallucination rates across LLMs, SLMs, and multi-agent swarms.
- Implement rigorous responsible-AI practices across the model lifecycle, establishing proactive bias detection, fairness constraints, data contracts, and model-risk controls.
- Partner with data and software engineering to integrate enterprise knowledge graphs and semantic layers, ensuring high-fidelity retrieval for retrieval augmented generation (RAG) pipelines and vector search/databases.
- Oversee Day-2 telemetry in collaboration with domain teams to monitor production inference data, proactively detecting concept drift, data degradation, and model decay.
Requirements
- 6 years of experience developing, evaluating, or applying modern AI/ML solutions, including LLMs, RAG, optimization, or causal methods, as relevant to the business problem
- 6 years of demonstrated understanding of AI evaluation, responsible-AI practices, and bias/risk mitigation
- 6 years of proven experience evaluating production GenAI and agentic systems using golden datasets, LLM-as-a-judge methodologies, guardrail hit rate analysis, and response failure analytics.
What Skills You Have
Required
- Bachelor’s Degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
- 6 years (or 3 years with a Master’s degree) of progressively complex data science experience
- Expertise in developing and deploying state-of-the-art algorithms using machine learning, statistical and optimization methods to power various aspects of highly complex business models and deliver value
- Expert in using modern analytics tools, programming languages, and cloud platforms (Python, R, Spark, SQL, GCP, etc.)
- Strong problem-solving skills with an emphasis on product development
- Experience proposing rapid experiments to test the effectiveness of new strategies or initiatives and iterating quickly
- Proven ability to guide teams through unstructured technical problems to deliver business impact
Preferred
- Master's degree and/or Ph.D.
- Retail and Logistics experience
- Merchandising and/or Supply Chain Management
- Digital and/or Marketing models