MillerKnoll-posted 3 months ago
Chicago, IL
5,001-10,000 employees

We’re looking for an experienced and adaptable Data Scientist to join our growing AI & Data Science team. You’ll be part of a small, highly technical group focused on delivering impactful machine learning, forecasting, and generative AI solutions. In this role, you’ll work closely with stakeholders to translate business challenges into well-defined analytical problems, design and validate models, and communicate results in clear, actionable terms. You’ll collaborate extensively with our ML Engineer to transition solutions from experimentation to production, ensuring models are both effective and robust in real-world environments. You’ll be expected to quickly prototype and iterate on solutions, adapt to new tools and approaches, and share knowledge with the broader organization. This is a hands-on role with real impact and room to innovate.

  • Partner with business stakeholders to identify, scope, and prioritize data science opportunities.
  • Translate complex business problems into structured analytical tasks and hypotheses.
  • Design, develop, and evaluate machine learning, forecasting, and statistical models, considering fairness, interpretability, and business impact.
  • Perform exploratory data analysis, feature engineering, and data preprocessing.
  • Rapidly prototype solutions to assess feasibility before scaling.
  • Interpret model outputs and clearly communicate findings, implications, and recommendations to both technical and non-technical audiences.
  • Collaborate closely with the ML Engineer to transition models from experimentation into scalable, production-ready systems.
  • Develop reproducible code, clear documentation, and reusable analytical workflows to support org-wide AI adoption.
  • Stay up to date with advances in data science, AI/ML, and generative AI, bringing innovative approaches to the team.
  • Bachelor’s or Master’s degree in Data Science, Statistics, Applied Mathematics, Computer Science, or a related quantitative field, with 3+ years of applied experience in data science.
  • Strong foundation in statistics, probability, linear algebra, and optimization.
  • Proficiency with Python and common data science libraries (Pandas, NumPy, Scikit-learn, XGBoost, PyTorch or TensorFlow).
  • Experience with time series forecasting, regression, classification, clustering, or recommendation systems.
  • Familiarity with GenAI concepts and tools (LLM APIs, embeddings, prompt engineering, evaluation methods).
  • Strong SQL skills and experience working with large datasets and cloud-based data warehouses (Snowflake, BigQuery, etc.).
  • Solid understanding of experimental design and model evaluation metrics beyond accuracy.
  • Experience with data visualization and storytelling tools (Plotly, Tableau, Power BI, or Streamlit).
  • Exposure to MLOps/LLMOps concepts and working in close collaboration with engineering teams.
  • Excellent communication skills with the ability to translate analysis into actionable business recommendations.
  • Strong problem-solving abilities and business acumen.
  • High adaptability to evolving tools, frameworks, and industry practices.
  • Curiosity and continuous learning mindset.
  • Stakeholder empathy and ability to build trust while introducing AI solutions.
  • Strong collaboration skills and comfort working in ambiguous, fast-paced environments.
  • Commitment to clear documentation and knowledge sharing.
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