Design, develop, and validate machine learning models that solve complex business problems through data
Translate functional and strategic requirements into predictive, prescriptive, and analytical solutions
Lead the end-to-end model lifecycle — from data exploration and feature engineering to deployment and monitoring
Collaborate with architects and engineers to ensure seamless integration of models into production environments
Apply best practices in MLOps, ensuring models are traceable, reproducible, and governed throughout their lifecycle
Evaluate and optimize model performance using robust metrics and explainability techniques
Document, communicate, and present insights clearly to both technical and business audiences
Mentor junior data scientists, promoting technical excellence, innovation, and a culture of learning within the Hub
Requirements
4+ years of experience developing predictive models and advanced analytics solutions in business contexts
Proven expertise across the entire model lifecycle — data preparation, modeling, validation, evaluation, and deployment
Strong programming skills in Python and core ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, StatsModels)
Solid foundation in statistical analysis, supervised and unsupervised learning, regression, classification, clustering, anomaly detection, and forecasting
Experience with feature engineering, data transformation, and data augmentation for model improvement
Knowledge of MLOps and CI/CD for models, using tools such as MLflow, DVC, Airflow, Kubeflow, or Vertex Pipelines
Skilled in model evaluation and optimization (ROC, AUC, F1, RMSE, SHAP, explainability techniques)
Familiarity with DataOps principles, APIs, ETL pipelines, and relational or NoSQL databases
Understanding of AI governance, privacy, and compliance frameworks (GDPR, Responsible AI)
Benefits
Professional development opportunities
Employee learning programs that give access to the best learning in the industry
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