MeridianLink
Predictive Analytics Consultant
US Remote
Sponsorship not specifiedDetected 68 days ago
PythonAlgorithmsSQLAWSMachine Learningscikit-learnPandasData ScienceStatisticsProject ManagementCommunicationProblem SolvingUnderwriting
About the role
- Additionally, they should have a strong grasp of how decisioning engines work in lending, banking, or credit union environments for consumer loans, including data integration and automated underwriting.
- Managing large, complex datasets from multiple sources, ensuring they are accurate, clean, and organized for analysis.
Responsibilities
- Managing large, complex datasets from multiple sources, ensuring they are accurate, clean, and organized for analysis. Perform detailed data wrangling tasks to handle data inconsistencies to prepare data for use in predictive models and analysis.
- Develop end-to-end analytical solutions, from data collection to model deployment, ensuring that the solutions meet the client's business objectives, such as improving lending strategies or underwriting decisions.
- Ensure that the analytical results align with key performance indicators (KPIs) and help drive measurable outcomes.
Requirements
- 4+ years of experience building and validating predictive credit risk models, preferably in the financial services or lending industry.
- Proven experience with model development and deployment, testing, validation, and monitoring.
- High-level proficiency and advanced skills in SQL for data querying and data manipulation.
- Strong project management skills with the ability to handle multiple tasks and deadlines.
- Expert-level skills in programming languages such as Python for model development and analysis leveraging Pandas, Scikit-learn, and other data handling, statistical, optimization, and machine learning frameworks
- Strong problem-solving skills and attention to detail in analyzing data and validating models.
- Excellent communication skills to present technical concepts to non-technical stakeholders.
- Ability to work independently and as part of a team in a fast-paced, dynamic environment.
Nice to have
- Bachelor's or Master's degree in Statistics, Data Science, Analytics, Mathematics, Economics, Finance, or a related field is preferred
- Proficiency in AWS for training, building, and deploying models is preferred, along with experience in MLOps.
Benefits
- Implement advanced data transformation techniques (e.g., feature engineering, aggregation, normalization) to optimize data for specific machine learning, optimization and statistical models.
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