Predictive Analytics Consultant

Responsibilities

• Manage 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.

Qualifications

• 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.
• Able 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.