Manager, Data & AI

CSI

About The Position

The Manager, Data & AI will lead a talented team of Data Scientists and Data Analysts, focusing on developing and deploying intelligent, data-driven solutions that provide measurable value to our products and customers. This position demands a blend of technical depth, strong leadership, and a collaborative spirit to build and integrate cutting-edge AI and analytics capabilities into CSI’s growing suite of products. What You'll Do 1. Lead & Mentor a High-Performing Team: o Manage, mentor, and grow a team of Data Scientists and Data Analysts, fostering a culture of continuous learning, technical excellence, and professional development. o Guide the team in adopting best practices for machine learning model development, deployment, and ongoing governance. 2 Architect & Develop Advanced Data Science Solutions: o Lead the design, development, and deployment of robust data models utilizing advanced statistical and machine learning techniques to generate demographic, behavioral, and predictive indicators from diverse data sources. o Lead the development and integration of production-ready AI-driven solutions into CSI’s products. o Collaborate closely with Architects and Data Warehouse Engineers to design scalable and maintainable data science and AI-driven solutions for complex data-driven business problems. 3. Drive Analytics & Product Insights: o Oversee the creation of high-quality analytics assets, including metrics, visualizations, and dashboards, for internal stakeholders and customer- facing products. o Work with designers and quality engineers to build analytical deliverables that are accurate, performant, and provide clear, actionable insights. 4. Strategic Advisory & Innovation: o Stay abreast of industry trends, emerging technologies, and academic advancements in data science, machine learning, and generative AI. Evaluate and champion the adoption of innovative methodologies and tools. o Champion responsible AI practices, ensuring model explainability, fairness, and adherence to relevant financial industry regulations regarding model risk management and data privacy. 5. Foster Cross-Functional Collaboration: o Drive effective collaboration with engineering (frontend and backend), product management, data architecture, and QA teams to ensure the successful end- to-end delivery and integration of data science and AI-driven solutions. o Communicate complex technical concepts and findings clearly and concisely to diverse audiences, including technical teams, product stakeholders, and executive leadership. As a forward-thinking software provider, Computer Services, Inc. (CSI) helps community and regional financial institutions solve their customers’ needs through open and flexible technologies. In addition to its award-winning core banking platform, these include the latest in lending, digital banking, payments, financial crime prevention and cybersecurity. Building on its 60-year track record of personalized service, CSI is shaping the future of banking and empowering its customers to rival their competition. For more information about CSI, visit www.csiweb.com

Requirements

5-8 years of progressive experience in data science, Machine Learning, or Advanced Analytics roles, with a proven track record of developing and deploy ing impactful solutions.
At least 2-3 years in a leadership, management, or senior technical lead capacity, guiding and mentoring data science or analytics professionals.
Experience in the FinTech or financial services industry is strongly preferred.
Bachelor’s or Master’s degree in a quantitative field such as data science, Computer Science, Statistics, Mathematics, or Operations Research.
Expert proficiency in Python (including libraries like scikit-learn, TensorFlow, PyTorch) and SQL for data manipulation, analysis, and model development.
Demonstrated experience with cloud platforms, specifically AWS, including familiarity with ML services like AWS SageMaker.
Hands-on experience with data warehousing technologies, preferably, but not limited to Snowflake.
Proficiency with data visualization and Business Intelligence tools, preferably, but not limited to Domo.
Solid understanding of machine learning algorithms, statistical modeling, experimental design (A/B testing), and model validation techniques.
Practical experience developing and integrating AI-driven solutions, including familiarity with Natural Language Processing (NLP) or Large Language Models (LLMs).
Knowledge of MLOps principles and practices for model lifecycle management, monitoring, and deployment.