Data Science (Machine Learning) for Paques

About the role

Design, implement, and optimize scalable data science solutions supporting executive decision-making. Develop machine learning models for prediction, classification, anomaly detection, forecasting, sentiment analysis and risk scoring. Analyze structured and unstructured datasets to uncover insights and emerging trends.

Key responsibilities

• Design, implement, and optimize scalable data science solutions supporting executive decision-making
• Develop machine learning models for prediction, classification, anomaly detection, forecasting, sentiment analysis and risk scoring
• Analyze structured and unstructured datasets to uncover insights and emerging trends
• Develop statistical models, predictive analytics and AI-driven recommendation systems
• Build, evaluate, deploy and monitor machine learning models in production environments
• Collaborate with AI Architects and Data Engineers to integrate machine learning with Generative AI and RAG pipelines
• Design experiments, evaluation metrics and continuous feedback mechanisms to improve model performance
• Ensure data science solutions are scalable, maintainable and production-ready
• Support the development of executive intelligence products through advanced analytics and AI
• Contribute to the adoption of best practices in machine learning, MLOps and responsible AI

About you

• Bachelor's degree in Engineering, Computer Science, Mathematics, Statistics, Data Science or related fields
• Minimum 5+ years of experience in data science, machine learning, predictive analytics or statistical modelling
• Strong knowledge of machine learning algorithms, natural language processing, time-series forecasting and statistical analysis
• Proficiency in Python and data science libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, XGBoost or LightGBM
• Experience working with SQL, large-scale datasets, feature engineering, and data visualization
• Hands-on experience with MLOps tools and production model deployment
• Familiarity with Generative AI, LLMs, Retrieval-Augmented Generation (RAG), embeddings and vector databases is highly preferred
• Strong understanding of AI governance, model evaluation, explainability and responsible AI principles
• Excellent analytical, problem-solving, communication and presentation skills