Data Scientist
Role Overview
As a Data Scientist in the Data & Artificial Intelligence division, design, develop, and deploy machine learning models and advanced analytics solutions.
The role focuses on actionable insights, operational AI, measurable business value, and scalable production-ready data science solutions.
Key Skills for This Role
Full Job Posting
Role overview
As a Data Scientist in the Data & Artificial Intelligence division, design, develop, and deploy machine learning models and advanced analytics solutions.
The role focuses on actionable insights, operational AI, measurable business value, and scalable production-ready data science solutions.
Core work
- Develop models and analytics using Python, Dataiku, and Azure Databricks.
- Prepare data, engineer features, develop models, deploy solutions, automate workflows, and monitor production systems.
- Deliver predictive, analytical, and AI solutions that translate data into business value.
Qualifications and experience
- A bachelor's degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering, or a related field is required; a master's degree is preferred.
- At least five years of hands-on experience in data science, machine learning, predictive analytics, and AI solution development is required.
- Strong Python skills, including Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow, and PyTorch, are required.
- Production Dataiku experience and experience with Azure Databricks, PySpark, MLflow, SQL, and large-scale data processing are required.
- Expertise in statistical and machine learning methods, feature engineering, validation, optimization, explainability, and multiple model types is required.
- Experience in NLP, text analytics, sentiment analysis, generative AI, and production model management is required.
- MLOps experience includes CI/CD, model versioning, experiment tracking, model registry, retraining, monitoring, drift detection, optimization, and governance.
Models and platforms
- Model types include regression, classification, clustering, forecasting, recommendation, customer segmentation, risk, and fraud detection.
- Production platforms and tools include MLflow, Databricks Model Serving, and Azure ML.
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