Data Scientist & Customer Success & Problem Solving

Role Overview: As a Data Scientist in Customer Success & Problem Solving, you will act as the senior technical point of contact, driving successful outcomes by identifying, scoping, and leading data science projects that directly address high-value customer problems. Key Responsibilities: - Act as the senior technical point of contact, driving successful outcomes by identifying, scoping, and leading data science projects that directly address high-value customer problems. - Measure and report on the business impact (ROI, efficiency gains) of deployed models. - Ensure rigorous testing and validation of all new data science features and model deployments. - Design, implement, monitor, and maintain scalable, production-grade Data Science systems. - Research and evaluate the latest in Machine Learning, Generative AI, and advanced statistical models for new feature development. - Actively scout, evaluate, and implement the latest advancements in Data Science, Machine Learning (MLOps, LLMs/Generative AI), and AI to maintain Aeras technical edge. - Take ownership of critical model performance issues and champion rapid resolution for Data Science models and pipelines. - Work collaboratively with Support, Customer Success, and Product teams to implement robust, long-term technical solutions. - Act as a technical advisor to the Product Team by sharing insights from field implementations and providing data-driven feedback on model performance and usability to help inform future improvements to Aera Decision Clouds Data Science capabilities. Qualifications Required: - Bachelors/Masters degree in Computer Science, Data Science, Statistics, or a related field. - Proven experience in leading data science projects and driving successful outcomes. - Strong expertise in Machine Learning, Generative AI, and advanced statistical models. - Excellent problem-solving skills and the ability to collaborate effectively with cross-functional teams. - Experience in maintaining scalable, production-grade Data Science systems. (Note: Any additional details of the company were not included in the job description provided.) Role Overview: As a Data Scientist in Customer Success & Problem Solving, you will act as the senior technical point of contact, driving successful outcomes by identifying, scoping, and leading data science projects that directly address high-value customer problems. Key Responsibilities: - Act as the senior technical point of contact, driving successful outcomes by identifying, scoping, and leading data science projects that directly address high-value customer problems. - Measure and report on the business impact (ROI, efficiency gains) of deployed models. - Ensure rigorous testing and validation of all new data science features and model deployments. - Design, implement, monitor, and maintain scalable, production-grade Data Science systems. - Research and evaluate the latest in Machine Learning, Generative AI, and advanced statistical models for new feature development. - Actively scout, evaluate, and implement the latest advancements in Data Science, Machine Learning (MLOps, LLMs/Generative AI), and AI to maintain Aeras technical edge. - Take ownership of critical model performance issues and champion rapid resolution for Data Science models and pipelines. - Work collaboratively with Support, Customer Success, and Product teams to implement robust, long-term technical solutions. - Act as a technical advisor to the Product Team by sharing insights from field implementations and providing data-driven feedback on model performance and usability to help inform future improvements to Aera Decision Clouds Data Science capabilities. Qualifications Required: - Bachelors/Masters degree in Computer Science, Data Science, Statistics, or a related field. - Proven experience in leading data science projects and driving successful outcomes. - Strong expertise in Machine Learning, Generative AI, and advanced statistical models. - Excellent problem-solving skills and the ability to collaborate effectively with cross-functional teams. - Experience in maintaining scalable, production-grade Data Science systems. (Note: Any additional details of the company were not included in the job description provided.)