Predictive Analytics Consultant

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 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.
• 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.