Location: For Those Who Work At Home - Various, Ohio 44145 ABOUT THE JOB (JOB BRIEF): The Quantitative Analytics Manager is primarily responsible for leading the implementation of predictive and machine-learning models for specific business needs for the model life cycle using statistics, advanced mathematical techniques, and/or computer science. The ideal candidate will have a deep understanding of statistical and machine learning models, as well as a proven track record of implementing and optimizing them for real-world applications. The ideal candidate will have strong analytics and data science application development skill set. The candidate will work closely with cross-functional teams, and your expertise will be pivotal in driving innovation through modeling and technological solutions. Projects undertaken by the Quantitative Analytics Manager are often broad in scope across multiple business segments and involve guiding a team and/or project through providing solutions to business problems leveraging statistics, best practices or emerging techniques, and quantitative tools / techniques. Success factors include: Demonstrating leadership through strong communication skills, addressing conflict, coaching others on developing technical skills; managing competing priorities and presenting holistic, thoughtful analyses to answer partners' problem statements; prioritizing multiple projects and managing to tight deadlines; establishing reputation as an effective and collaborative partner; Communicating technical theories, observations, and models to a non-technical audience; Leveraging knowledge of strategy, business, and competition to connect day-to-day work of team to the "bigger picture" and driving efficiency in solution delivery ESSENTIAL JOB FUNCTIONS Develop, implement, and optimize production-ready code for statistical and machine learning models. Ensure that these models are robust, scalable, and efficient for real-world applications. Often responsible for large, complex problems that have broad implications and are less frequent Conceptualize and architect software solutions tailored for advanced modeling and analytics tasks. This involves understanding the specific requirements of the model, ensuring that the software is modular, scalable, and integrates well with existing systems. Design, automate, and deploy a seamless machine learning pipelines to ensure smooth transition from development to production environments. Implement machine learning and data engineering solutions predominantly in Google Cloud. Experience with Google Cloud products such as BigQuery, Cloud ML Engine, and Dataflow will be a significant advantage. Reviews deliverables; proactively coaches others on approach and work product Lead and evangelize on best practices of capturing and retaining data Work closely with cross-functional teams, including product managers, software engineers, and data scientists. Provide technical expertise on projects that require data-driven solutions. Stay updated with the latest industry trends in data engineering, machine learning, and cloud computing. Leverage new technologies and methodologies to drive continuous innovation. REQUIRED QUALIFICATIONS Master's degree (or its equivalent) in statistics, mathematics, economics, financial engineering, data sciences, predictive modeling, computer science or other quantitative disciplines. 7+ years of experience in data science, analytics, or a similar role, with a proven track record of delivering impactful results. Strong expertise in machine learning, statistical modeling, and data mining techniques. Proficiency in programming languages such as Python, R, Java, and C++, with experience in Python Flask for web application development. Experience with data manipulation and visualization tools (e.g., SQL, Pandas, Tableau). Experience with big data technologies such as Hadoop, Spark, and