ML Engineering / Data Scientist

As a skilled ML Engineer Data Scientist, you will be responsible for designing, developing, and deploying advanced machine learning models and data products. Your work will involve diverse projects including statistical modeling, deep learning, and production-grade ML pipelines to drive impactful business decisions. Key Responsibilities: - Develop and implement ML models for regression, classification, clustering, time series, recommender systems, and more. - Work with advanced AI techniques including deep learning, NLP, reinforcement learning, and federated learning. - Apply statistical modeling and algorithms such as hypothesis testing, A/B testing, and stochastic simulations. - Program using Python, R, SQL, and Spark to build scalable data pipelines and ML workflows. - Utilize ML frameworks like TensorFlow, Keras, and PyTorch for model development and training. - Deploy and monitor ML models in cloud environments, preferably Azure, to ensure reliability and scalability. - Collaborate with cross-functional teams to translate data insights into actionable solutions. Qualifications: - Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Mathematics, Economics, Statistics, or Data Science. - Strong knowledge of ML algorithms such as regression, decision trees, random forests, SVM, neural networks, transformers, etc. - Proficiency in Python, R, SQL, and Spark. - Experience with ML libraries and frameworks like TensorFlow, Keras, PyTorch. - Expertise in statistical methods and mathematical programming. - Familiarity with cloud services, preferably Azure. - Ability to deploy, monitor, and maintain ML models in production. - Robust analytical problem-solving and communication skills. If you are interested in this opportunity, please apply by sending your CV to the provided email address. As a skilled ML Engineer Data Scientist, you will be responsible for designing, developing, and deploying advanced machine learning models and data products. Your work will involve diverse projects including statistical modeling, deep learning, and production-grade ML pipelines to drive impactful business decisions. Key Responsibilities: - Develop and implement ML models for regression, classification, clustering, time series, recommender systems, and more. - Work with advanced AI techniques including deep learning, NLP, reinforcement learning, and federated learning. - Apply statistical modeling and algorithms such as hypothesis testing, A/B testing, and stochastic simulations. - Program using Python, R, SQL, and Spark to build scalable data pipelines and ML workflows. - Utilize ML frameworks like TensorFlow, Keras, and PyTorch for model development and training. - Deploy and monitor ML models in cloud environments, preferably Azure, to ensure reliability and scalability. - Collaborate with cross-functional teams to translate data insights into actionable solutions. Qualifications: - Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Mathematics, Economics, Statistics, or Data Science. - Strong knowledge of ML algorithms such as regression, decision trees, random forests, SVM, neural networks, transformers, etc. - Proficiency in Python, R, SQL, and Spark. - Experience with ML libraries and frameworks like TensorFlow, Keras, PyTorch. - Expertise in statistical methods and mathematical programming. - Familiarity with cloud services, preferably Azure. - Ability to deploy, monitor, and maintain ML models in production. - Robust analytical problem-solving and communication skills. If you are interested in this opportunity, please apply by sending your CV to the provided email address.