Job Description & How to Apply Below
Responsibilities:
Own model development, experimentation, evaluation, and production readiness.
Develop forecasting, optimization, recommendation, decision-science, and ML models.
Perform feature engineering, EDA, and statistical analysis to identify meaningful patterns.
Design rigorous model evaluation, validation, and testing frameworks.
Validate model performance, fairness, and bias before go-live.
Manage experiment tracking, datasets, and model reproducibility using tools such as MLflow/W&B.
Present model results, trade-offs, and uncertainty to business stakeholders.
Collaborate with ML Engineers and Architects to product ionize models.
Lead complex decision-intelligence engagements when required.
Mentor junior Data Scientists and ML Engineers.
Qualifications:
5+ years of applied Data Science experience with production model-shipping experience.
Strong expertise in classical/statistical ML, forecasting, optimization, and deep learning.
Experience with NLP, Computer Vision, graph-based features, or knowledge graphs is a plus.
Strong Python, SQL, and experience with scikit-learn, PyTorch/Tensor Flow, MLflow, or Weights & Biases.
Strong knowledge of hypothesis testing, confidence intervals, and causal inference.
Familiarity with fairness/bias evaluation and explainability tools such as SHAP and LIME.
Strong communication skills with the ability to explain technical concepts and model trade-offs to business stakeholders.
Ability to defend modeling decisions and collaborate closely with engineering and architecture teams.
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