Data Science & AI Implementer

As an AI Engineer, you will be responsible for utilizing data visualization techniques to drive insights in the energy sector. Your key responsibilities will include implementing data science solutions using machine learning, NLP, Gen AI, and deep learning techniques to optimize business outcomes. You will also evaluate the quality and effectiveness of data sources and collection methodologies. Additionally, translating complex technical concepts into actionable insights will be part of your role. Your technical expertise should include proficiency in cloud platforms such as Azure (Azure Machine Learning, Azure Databricks, Azure Storage) and programming languages like Python (Pandas, NumPy). You should be skilled in developing and deploying machine learning models for supervised and unsupervised learning, regression, classification, clustering, and model evaluation and selection. Utilizing optimization techniques for asset performance improvement and developing deep learning models including CNNs, RNNs, and LSTM networks will be essential. Moreover, exploring and applying generative AI techniques like GANs, VAEs, and Transformers will also be part of your responsibilities. Qualifications required for this role include a Bachelor's degree in computer science, Data Science, Statistics, Mathematics, or a related field; a masters degree is preferred. You should have 4-6 years of hands-on experience in data science or a related role, with a successful track record of project delivery. As an AI Engineer, you will be responsible for utilizing data visualization techniques to drive insights in the energy sector. Your key responsibilities will include implementing data science solutions using machine learning, NLP, Gen AI, and deep learning techniques to optimize business outcomes. You will also evaluate the quality and effectiveness of data sources and collection methodologies. Additionally, translating complex technical concepts into actionable insights will be part of your role. Your technical expertise should include proficiency in cloud platforms such as Azure (Azure Machine Learning, Azure Databricks, Azure Storage) and programming languages like Python (Pandas, NumPy). You should be skilled in developing and deploying machine learning models for supervised and unsupervised learning, regression, classification, clustering, and model evaluation and selection. Utilizing optimization techniques for asset performance improvement and developing deep learning models including CNNs, RNNs, and LSTM networks will be essential. Moreover, exploring and applying generative AI techniques like GANs, VAEs, and Transformers will also be part of your responsibilities. Qualifications required for this role include a Bachelor's degree in computer science, Data Science, Statistics, Mathematics, or a related field; a masters degree is preferred. You should have 4-6 years of hands-on experience in data science or a related role, with a successful track record of project delivery.