Data Scientist

Role overview

Education and experience

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering, or a related field; a master’s degree is preferred.
• At least 5 years of hands-on experience in data science, machine learning, predictive analytics, and AI solution development.

Platforms and programming

• Strong Python expertise, including Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow, and PyTorch.
• Production Dataiku experience spanning data preparation, feature engineering, model development, deployment, automation, and monitoring.
• Experience with Azure Databricks, PySpark, MLflow, large-scale distributed processing, and SQL on structured and unstructured datasets.

Data science and MLOps

• Knowledge of machine learning, statistical modeling, predictive analytics, data mining, feature engineering, validation, hyperparameter optimization, and explainability.
• Experience building regression, classification, clustering, forecasting, recommendation, customer segmentation, risk, and fraud detection models.
• Hands-on NLP, text analytics, sentiment analysis, and generative AI experience.
• Production deployment with MLflow, Databricks Model Serving, or Azure ML; MLOps experience includes CI/CD, versioning, monitoring, drift detection, retraining, optimization, and governance.

Responsibilities

• Design, develop, and deploy machine-learning models and advanced analytics solutions using Python, Dataiku, and Azure Databricks.
• Transform data into actionable insights and deliver scalable, production-ready data science solutions with measurable business value.
• Use Dataiku for data preparation, feature engineering, model development, deployment, automation, and monitoring.
• Develop and validate predictive models, including regression, classification, clustering, forecasting, recommendation, segmentation, risk, and fraud detection models.
• Deploy and manage production models using MLflow, Databricks Model Serving, and Azure Machine Learning.
• Operationalize MLOps workflows, including CI/CD, versioning, experiment tracking, automated retraining, model monitoring, drift detection, optimization, and governance.
• Apply NLP, text analytics, sentiment analysis, and generative AI techniques to data science solutions.