Senior Data Scientist

Join our team as a Senior Data Scientist, where you will design, develop, and deploy machine learning and statistical models to forecast customer cash flows, credit risk, and other business health measures. You will explore and analyze large datasets, research advanced deep learning architectures, and monitor models in production. This is a remote-first position with flexible work options and a comprehensive benefits package.

• Design, develop, and deploy machine learning and statistical models to forecast customer cash flows and credit risk.
• Explore and analyze large datasets to identify relevant signals, engineer features, and uncover insights that inform model and product design.
• Research and evaluate advanced deep learning architectures and training techniques to improve core underwriting algorithms.
• 3 years in the following:
• Designing, training and evaluating deep learning models for sequence and time series data, including transformer based architectures and recurrent neural networks, applied to forecasting or similar domains
• Experimentation, including design, execution and analysis of A/B tests and offline and online experiments in production environments
• Machine learning, deep learning, optimization, statistics and probability theory, including the design and analysis of loss functions, weight initialization schemes and neural network architectures under computational and data constraints
• Large scale data processing and model training using modern machine learning frameworks such as PyTorch, TensorFlow, JAX, scikit learn, MXNet or Spark, and cloud platforms such as AWS or GCP
• Executing end to end machine learning projects, including data collection and preprocessing, feature engineering, model development, deployment to production systems and ongoing performance monitoring
• Statistical and causal inference methods, including probabilistic graphical models, Bayesian inference, difference in differences or propensity score based methods, to estimate the impact of business or product interventions
• Building and optimizing deep learning models for forecasting, classification and ranking that predict key user, product or business outcomes, including definition and improvement of model performance metrics such as accuracy, AUC, RMSE or MAPE