
Senior Data Scientist - AI and Generative AI
- Naperville, IL
- $143,000-187,740 per year
- Permanent
- Part-time
- Advanced Modeling & AI Innovation – Develop and enhance decision models using probabilistic, machine learning, and generative AI techniques. Lead large-scale, high-value projects that push the boundaries of statistical and AI capabilities, leveraging platforms like Databricks for model development, distributed data processing, and deployment pipelines.
- Stakeholder Collaboration & Model Transparency – Partner with business experts to understand challenges and pilot proof-of-concept models using diverse data types. Clearly communicate model outputs and rationale to build trust and foster adoption of AI/GenAI solutions.
- AI Infrastructure, MLOps & Analytics Strategy – Recommend scalable solutions for big data and GenAI infrastructure, applying MLOps practices such as CI/CD for ML, model versioning, monitoring, and governance to ensure robust and scalable deployment of AI/GenAI models.
- Enablement, Mentorship & Responsible AI Adoption – Drive adoption of AI tools and models through training and support. Mentor junior team members and promote a culture of innovation and responsible AI use across the organization.
- Master’s degree in Data Science, AI, Computer Science, Statistics, Mathematics, or a related field—or equivalent professional experience or certification.
- Demonstrated success applying data science and AI/GenAI solutions in consumer-centric or relevant industries.
- Proficient in Python, R, or similar languages, with hands-on experience using machine learning frameworks and GenAI libraries such as Hugging Face, LangChain, LangGraph, PyTorch, or TensorFlow.
- Skilled in data analysis, algorithm development, machine learning, and generative AI—including large language models, diffusion models, and multimodal AI.
- Proven ability to design, build, and operationalize AI/GenAI applications like chatbots, recommendation systems, automated content generation, and decision-support tools.
- Strong understanding of the AI/ML model lifecycle (training, fine-tuning, deployment, monitoring, governance) with a focus on responsible AI and ethical considerations.
- Capable of translating complex technical concepts into clear insights for technical and non-technical audiences, fostering trust and adoption across stakeholder groups.