Data Scientist

Description

• •Design, develop, and evaluate machine learning, statistical, and predictive models to solve complex business challenges across multiple departments.
• •Apply modern artificial intelligence and machine learning techniques, including large language models (LLMs), generative AI, and advanced analytics, to automate processes, enhance decision-making, and generate business insights.
• •Translate business objectives into well-defined analytical, statistical, and machine learning solutions that deliver measurable business value.
• •Analyze large, complex datasets to identify trends, patterns, opportunities, and operational improvements.
• •Partner with data engineering, IT, and business teams to develop scalable data pipelines and deploy machine learning models into production environments.
• •Evaluate data quality, model performance, and AI system limitations while ensuring responsible, ethical, and practical implementation of predictive models.
• •Present analytical findings, recommendations, and technical concepts clearly to executive leadership and both technical and non-technical stakeholders.
• •Develop, monitor, and optimize predictive models, ensuring ongoing performance, accuracy, and reliability through continuous improvement.
• •Stay current with emerging technologies, AI advancements, machine learning methodologies, and data science best practices to identify opportunities for innovation.
• •Collaborate across departments to support strategic initiatives, business intelligence projects, forecasting, automation, and operational optimization.
• •Maintain thorough documentation of models, methodologies, assumptions, and development processes to support transparency, reproducibility, and governance.
• •Support ad hoc analytical projects and provide data-driven recommendations that improve business performance and operational efficiency.

Requirements

• •Bachelor's degree required in Mathematics, Data Science, Computer Science, Engineering, Physics, or another quantitative discipline; advanced degree preferred.
• •Strong technical foundation in statistics, predictive modeling, machine learning algorithms, and programming languages such as Python and SQL.
• •Demonstrated experience working with modern AI technologies, including deep learning, large language models (LLMs), generative AI, MLOps, or related machine learning frameworks.
• •Experience developing, deploying, and maintaining machine learning models in production environments.
• •Strong understanding of cloud computing platforms and modern data science tools and technologies.
• •Ability to evaluate model performance, balance trade-offs between accuracy, interpretability, speed, and risk, and apply sound judgment in ambiguous situations.
• •Experience communicating complex technical concepts to business leaders and collaborating effectively with cross-functional teams.
• •Experience within financial services, mortgage lending, or other highly regulated industries preferred.
• •Familiarity with model governance, model risk management, compliance, or regulatory frameworks is a plus.