| Data Scientist at Remote, Remote, USA |
| Email: [email protected] |
| From: clara, Yochana IT solutions [email protected] Reply to: [email protected] Responsibilities: Design, develop, and implement Gen AI models for various applications, focusing on tasks like data augmentation, anomaly detection, and content generation. Collaborate with engineers to integrate Gen AI models into existing systems and workflows utilizing AKS, Cosmos DB, and Synapse Analytics. Participate in the entire machine learning pipeline, including data preparation, feature engineering, model training, evaluation, and deployment. Monitor and evaluate the performance of Gen AI models, identifying opportunities for improvement and optimization. Stay up-to-date on the latest advancements in Gen AI and related fields, actively learning and applying new techniques. Participate in the development and implementation of best practices for responsible AI development and deployment. Collaborate effectively with cross-functional teams, including engineers, product managers, and data analysts, to translate business needs into technical solutions. Communicate complex technical concepts clearly and concisely to both technical and non-technical audiences. Qualifications: Ph.D. or Master's degree in Data Science, Computer Science, Statistics, or a related field (or equivalent experience). Proven experience in designing, developing, and deploying machine learning models, preferably with a focus on Generative AI techniques. Strong understanding of cloud-based computing platforms, particularly Azure AKS, Cosmos DB, and Synapse Analytics. Familiarity with data pipelines, data visualization tools, and version control systems (e.g., Git). Excellent problem-solving, analytical, and critical thinking skills. Strong communication and collaboration skills, with the ability to effectively work with diverse teams. Passion for innovation and a continuous learning mindset. Keywords: artificial intelligence database information technology Data Scientist [email protected] |
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| Thu Mar 28 23:29:00 UTC 2024 |