Champions Funding LLC
Linkedin · Posted 21d ago
Data Scientist
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Indexed description
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.
- 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.
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