Data Scientist

Champions Funding LLC; Gilbert (AZ); On-site; USD 120,000 - 160,000; Full time

Job summary

Champions Funding LLC is seeking a data science leader to design, develop, and deploy advanced machine learning models across multiple business units. You will translate strategic objectives into measurable analytics and work with data engineering and IT to build scalable pipelines.

The role requires strong Python/SQL skills, experience with ML in production, and familiarity with cloud platforms and regulatory considerations.

Qualifications

• Bachelor's degree required in Mathematics, Data Science, Computer Science, Engineering, Physics, or another quantitative discipline.
• Strong technical foundation in statistics, predictive modeling, machine learning algorithms, and programming languages such as Python and SQL.
• Demonstrated experience with modern AI technologies, including deep learning, large language models (LLMs), generative AI, MLOps, or related ML 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.

Responsibilities

• 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.

Skills

Python

SQL

Statistics

Predictive Modeling

Machine Learning

MLOps

Deep Learning

LLMs

Education

Bachelor's degree in a quantitative discipline

Advanced degree preferred

Tools

AWS

Azure

GCP

Job 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.

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.