Data Scientist

Requires a Master's Degree in Data Scientist and 1 year of experience. Experience as a Software Engineer is acceptable, or any suitable combination of education, training or experience thereof.

Position is for a Data Scientist (internal title: Research Scientist) responsible for designing, building, and implementing enterprise-level data warehouses and data lakes for corporate clients. Duties include, but are not limited to: Build and maintain data warehouses, data lakes, and modern analytics platforms to support high-volume data processing and reporting; data science techniques, including predictive modeling, clustering, and regression, to extract insights and support informed decision-making; Leverage advanced AI/ML approaches including deep learning, NLP, unsupervised learning, and reinforcement learning to improve forecasting accuracy, automate processes, and generate business insights; Lead the design and development of scalable data pipelines and ETL workflows, ensuring data is accurate, accessible, and ready for analytics; Engage with clients to understand business challenges and translate them into practical, data-driven solutions; Implement generative AI and Retrieval-Augmented Generation (RAG) systems to enhance analytics applications and automate knowledge retrieval; Collaborate with business analysts, architects, and data scientists to deliver integrated solutions combining traditional data engineering with advanced AI capabilities; Ensure data quality, governance, and compliance by implementing validation frameworks and monitoring systems for large-scale data operations; Migrate legacy systems and mainframe workflows to scalable cloud or hybrid platforms to improve performance and support modernization initiatives; Develop interactive dashboards and reporting solutions to provide visibility into KPIs, operational metrics, and analytical outputs; Integrate big data processing frameworks to manage high-volume structured and unstructured datasets and improve analytical efficiency; Conduct performance optimization for data pipelines, ML workloads, and cloud systems to reduce latency and improve scalability; Implement CI/CD workflows for data engineering and machine learning components to ensure consistent deployment and version control; Document system architectures, data workflows, and AI model behavior to support knowledge transfer and regulatory compliance; and stay current with emerging technologies, including Agentic AI, cloud-native services, and next-generation ML frameworks, and evaluate their applicability to enterprise use cases.

Jobsite: Position allows for hybrid employment (three days on-site and two days remote), but must be willing to travel to various unanticipated jobsites within the U.S. Frequency of travel is unanticipated.