MillerKnoll is looking to solve complex business problems using data science, machine learning, and AI solutions to drive business impact and support causes that align with their values.
Requirements
- 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).
Responsibilities
- 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.
Other
- 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.
- Commitment to clear documentation and knowledge sharing.
- 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.
