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.
Implement advanced data transformation techniques (e.g., feature engineering, aggregation, normalization) to optimize data for specific machine learning, optimization and statistical models.
Work on various types of predictive models, including classification, regression, and clustering, using algorithms like decision trees, random forests, or neural networks.
Contribute to the fine-tuning of models by optimizing hyperparameters and evaluating performance using cross-validation, ensuring that models meet business and technical requirements.
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.
Participate in the internal development of new data science methodologies that address evolving needs related to underwriting for our financial institution clients.
Present complex analytical findings in a clear and actionable format to internal stakeholders and external clients helping them interpret the results of predictive models and make informed decisions based on data insights.
Provide feedback and contribute to the continuous improvement of the data science workflow, ensuring projects are executed efficiently and with precision.
Requirements
Bachelor's or Master's degree in Statistics, Data Science, Analytics, Mathematics, Economics, Finance, or a related field is preferred
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.
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
High-level proficiency and advanced skills in SQL for data querying and data manipulation.
Proficiency in AWS for training, building, and deploying models is preferred, along with experience in MLOps.
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.
Strong project management skills with the ability to handle multiple tasks and deadlines.
Additional Information
The Predictive Analytics Consultant will be a key member of the analytics team, responsible for leading the design and delivery of critical solutions like Automated Underwriting, Risk Scoring, and Portfolio Monitoring. This role involves utilizing advanced data science tools and techniques to provide analytics services that optimize decision engines and improve the lending operations of financial institutions. The position will focus on building, testing, validating, and deploying predictive and optimization models that support credit underwriting decisions. The consultant will develop data-driven solutions to enhance credit risk assessments, automate decision-making, and optimize underwriting processes.
The ideal candidate should possess a thorough understanding of loan origination systems, core underwriting practices, credit bureau data, and the interaction between these areas to drive predictive analytics. 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.
Meridianlink · Remote