- Company Name
- MillerKnoll
- Job Title
- Data Scientist
- Job Description
-
**Job Title:** Data Scientist
**Role Summary:**
Deliver end‑to‑end machine learning and forecasting solutions that address business challenges. Collaborate closely with stakeholders to define problems, design models, prototype quickly, validate, and transition production systems in partnership with ML Engineers. Communicate findings clearly to both technical and non‑technical audiences and contribute to organizational AI adoption.
**Expectations:**
* 3+ years of applied data science experience.
* Proven ability to translate business objectives into analytical solutions and communicate actionable insights.
* Comfortable working in a fast‑paced, evolving environment with ambiguous requirements.
* Willingness to keep pace with emerging data science, AI, and generative‑AI technologies.
**Key Responsibilities:**
* Partner with business stakeholders to identify and prioritize data science opportunities.
* Translate complex business problems into hypothesised analytical tasks.
* Design, develop, evaluate, and iterate on machine learning, forecasting, statistical, and generative‑AI models while considering fairness, interpretability, and business impact.
* Conduct exploratory data analysis, feature engineering, and data preprocessing on large, cloud‑based datasets.
* Rapidly prototype and validate solution feasibility before scaling.
* Interpret model outputs, distill insights, and present findings with clear business recommendations.
* Collaborate with ML Engineers to move models from experimentation to scalable, production‑ready systems.
* Produce reproducible code, comprehensive documentation, and reusable analytical workflows.
* Stay current on industry advances in data science, AI/ML, and generative‑AI; introduce innovative approaches.
**Required Skills:**
*Technical:*
- Strong foundation in statistics, probability, linear algebra, and optimisation.
- Proficient in Python (Pandas, NumPy, Scikit‑learn, XGBoost, PyTorch/TensorFlow).
- Experience with time‑series forecasting, regression, classification, clustering, or recommendation engines.
- Familiarity with generative‑AI concepts and tools (LLM APIs, embeddings, prompt engineering, evaluation).
- Advanced SQL and handling of large datasets in cloud warehouses (Snowflake, BigQuery, etc.).
- Knowledge of experimental design, model‑evaluation metrics beyond accuracy (e.g., ROC‑AUC, precision‑recall, Fairness metrics).
- Data‑visualisation skills (Plotly, Tableau, Power BI, Streamlit).
- Exposure to MLOps/LLMOps practices and collaboration with engineering teams.
*Soft:*
- Excellent written and verbal communication, capable of translating technical results to non‑technical audiences.
- Strong problem‑solving, business acumen, and adaptability to new tools and frameworks.
- Curiosity and commitment to continuous learning.
- Empathy for stakeholders and ability to build trust when deploying AI solutions.
- Collaboration in ambiguous, high‑velocity contexts; documentation‑focused mindset.
**Required Education & Certifications:**
* Bachelor’s or Master’s degree in Data Science, Statistics, Applied Mathematics, Computer Science, or related quantitative field.
* No mandatory certifications, but knowledge of industry‑relevant ML or AI certifications (e.g., TensorFlow Developer, AWS AI/ML Specialty) is an asset.