Associate Data Scientist

Data Scientist IIntercontinental Exchange is seeking a Data Scientist I to join our Loan Data team. As a Data Scientist I, you will play a critical role in analyzing complex loan data sets to identify trends, develop data products, and drive business insights that inform our clients' investment decisions and business strategies. You will work closely with our team to design, develop, and implement data-driven solutions that meet the evolving needs of our clients and drive growth in the loan market.Intercontinental Exchange (ICE) is a global network of exchanges and clearing houses that operates a range of markets, including financial, agricultural, and energy. Our exchanges, including the New York Stock Exchange (NYSE), ICE Futures, and ICE Clear, provide a platform for buyers and sellers to trade and manage risk. We are committed to delivering innovative solutions that meet the evolving needs of our clients and drive growth in the global economy.ResponsibilitiesLoan Data Analysis:Collect, process, and analyze large loan data sets from various sources (e.g., loan origination systems, servicing platforms).Identify trends, patterns, and anomalies in loan data using statistical techniques and data visualization tools.Develop and maintain databases and data systems to support loan data analysis and modeling.Data Quality & Data StewardshipMonitor and assess the quality of loan data across internal and external sources.Identify data quality issues, anomalies, and inconsistencies through analysis and validation checks.Partner with data providers, clients, developers, and stakeholders to investigate root causes and drive issue remediation.Contribute to the development of data quality rules, monitoring processes, and controls.Document data quality issues and track resolution progress.Insight Generation:Communicate complex loan data insights and model results to non-technical stakeholders.Develop and present reports, dashboards, and visualizations to support business decisions.Collaborate with stakeholders to identify business problems and develop data-driven solutions.Data Engineering:Design, develop, and maintain data pipelines and architectures to support loan data analysis and modeling.Collaborate with data engineers to implement data solutions and integrate with existing systems.Research and Development:Stay up-to-date with industry trends, research, and developments in data science, machine learning, and related fields.Explore new technologies, tools, and methodologies to enhance existing loan data solutions and models.Knowledge and ExperienceBachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.Proficiency in programming languages (e.g., Python, R, SQL) and software tools (e.g., pandas, NumPy, scikit-learn).Experience with loan data analysis, machine learning, and data visualization.Familiarity with relational database tools and environments such as SSMS, along with exposure to modern data platforms such as Databricks or Snowflake.Experience with AI-assisted development and analytics tools and ability to incorporate them into data analysis and workflow processes.Strong analytical and problem-solving skills, with the ability to analyze complex loan data sets and develop creative solutions.Experience with data visualization tools (e.g., Sigma, Tableau, Power BI).Excellent communication and collaboration skills, with the ability to work effectively with technical and non-technical stakeholders.Nice to HaveAdvanced Degree: Master's or Ph.D. in Computer Science, Statistics, Mathematics, Data Science, or a related field.Certifications: Certified Data Scientist (CDS), Certified Analytics Professional (CAP), or other relevant certifications.Cloud Computing: Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud, Snowflake).Domain Expertise: Familiarity with loan markets, loan products, and lending regulations.Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.