Sr. Delivery Consultant - AI/ML, AWSI Energy SDT

Join AWS Professional Services' Energy segment in Houston as a Data Scientist and help global enterprises turn complex data into real business impact with AI. You’ll design, build, validate, and deploy end-to-end machine learning and deep learning solutions on AWS—working alongside Big Data and DevOps specialists to take models from discovery through production. This is an opportunity to solve high-value customer problems using AWS AI services, modern ML/DL frameworks, and large-scale cloud infrastructure, while exploring emerging approaches in areas such as model monitoring and retraining. If you are curious, customer-focused, love diving deep into data, and want to grow with a collaborative team at the forefront of cloud AI, this role offers meaningful technical challenges, mentorship, and strong career development opportunities in a high-growth industry segment for AWS cloud. Key job responsibilities As an experienced Professional Services Sales Leader, you will be responsible for: • Leading and developing a high-performing team of Account Executives and Cloud Architects • Developing and supporting execution of sales strategies to meet team targets and drive revenue growth • Managing customer relationships, ensuring high satisfaction and identifying new opportunities • Leveraging deep AWS knowledge to guide sellers in proposing optimal solutions • Monitoring and analyzing key performance indicators, ensuring accurate reporting and forecasting to optimize operational processes and manage risk A day in the life As an AWS Professional Services Data Scientist, you will partner with enterprise and public-sector customers to identify high-value AI/ML opportunities and translate business challenges into scalable cloud solutions. Day to day, you will work with customer business leaders, data teams, IT teams, and product owners to assess data, build and validate ML/DL models, and communicate results. You will collaborate with AWS consultants from other specialty disciplines to deploy, monitor, and retrain models. Typical problems include forecasting, fraud or anomaly detection, personalization, document automation, and operational optimization.