Company
financial institutions
PredictiveAnalyticsConsultant
Neural analysis suggests this role is
optimal for Mid candidates.
“Predictive Analytics Consultant. Skills: Predictive Analytics, Data Science, Machine Learning, Model Development, SQL, Python. leading the design and delivery of critical solutions like Automated Underwriting, Risk Scoring, and Portfolio Monitoring. utilizing advanced data science tools and techniques to provide analytics services that optimize decision engines and improve the lending operations of financial institutions”
What You'll Achieve.
optimize decision engines; improve the lending operations of financial institutions; support credit underwriting decisions; enhance credit risk assessments; automate decision-making; optimize underwriting processes; meet business and technical requirements; meet the client's business objectives; improving lending strategies; underwriting decisions; drive measurable outcomes
Industry & Context.
problem-solving skills; analyzing data; validating models
What They're Looking For.
Must Have
4+ years of experience building and validating predictive credit risk models, 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
Nice to Have
Bachelor’s or Master’s degree in Statistics, Data Science, Analytics, Mathematics, Economics, Finance, or a related field is preferred, preferably in the financial services or lending industry, Proficiency in AWS for training, building, and deploying models is preferred, along with experience in MLOps
What You'll Do.
leading the design and delivery of critical solutions like Automated Underwriting
and Portfolio Monitoring
utilizing advanced data science tools and techniques to provide analytics services that optimize decision engines and improve the lending operations of financial institutions
and deploying predictive and optimization models that support credit underwriting decisions
develop data-driven solutions to enhance credit risk assessments
automate decision-making
and optimize underwriting processes
complex datasets from multiple sources
ensuring they are accurate
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.
normalization) to optimize data for specific machine learning
optimization and statistical models
Work on various types of predictive models
including classification
using algorithms like decision trees
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
How You'll Work.
Team & Collaboration
Ability to work as part of a team; Present complex analytical findings in a clear and actionable format to internal stakeholders and external clients
Communication Scope
Excellent communication skills to present technical concepts to non-technical stakeholders
Process & Methodology
project management skills, Ability to handle multiple tasks and deadlines
Full Job Description
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. 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. - 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 c
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