• Design and develop models to extract entities, detect intents, and understand document structure • Tackle challenges like long-context reasoning, layout-aware NLP, and ambiguous inputs • Evaluate model performance where ground truth is partial, uncertain, or evolving • Shape the roadmap and success metrics for replacing legacy document processing systems with smarter, scalable solutions • Work with other data scientists and engineers to optimize machine learning models and insert them into end-to-end pipelines • Understand product use-cases and define key performance metrics for models according to business requirements • Set up systems for long-term improvement of models and data quality (e.g. active learning, continuous learning systems, etc.)

• Conduct experimentation and execute causal inference analyses on pricing, marketing, and conversion models to drive revenue optimization. • Develop Pricing Optimization algorithms to maximize the unit economics of our lending products while driving meaningful growth in our origination businesses. • Develop and maintain predictive ML models to assess potential risks and opportunities across our lending products, contributing to the enhancement of risk and marketing assessment procedures. • Conceptualize, research, and prototype data-driven solutions, effectively communicating their impact to the stakeholders. • Collaborate closely with cross-functional teams and stakeholders to accelerate solution iteration and achieve measurable outcomes. • Build funnel dashboards and perform root cause analysis to monitor and identify user behavioral patterns and areas of opportunity. • Collaborate with data and infrastructure engineers to deploy ML and Pricing pipelines, from data collection through model deployment. This includes automating training and ongoing monitoring utilizing BI tools. • Prepare technical designs and documentation using git and Confluence.

• Understand complex business challenges and translate them into data science problems. • Explore, analyze, clean, and model structured and unstructured datasets from various sources. • Design and implement machine learning models, statistical tests, and optimization strategies. • Conduct experimentation using A/B testing, uplift modeling, and simulation frameworks. • Deliver Proof-of-Concepts and production-ready models within Agile and cross-functional teams. • Create and present data storytelling insights for both technical and business stakeholders. • Contribute to model explainability, fairness, and responsible AI practices. • Stay updated on academic and industry research, and promote innovative solutions.

• Analyze unstructured and semi-structured data using advanced computational methods • Develop and implement algorithms for large-scale data analysis on distributed and cloud-based infrastructures • Process and index high-volume data collections and high-velocity data streams • Utilize advanced tools to interpret, connect, predict, and derive insights from complex data • Apply machine learning, algorithm analysis, and data clustering techniques • Support software development using open-source and enterprise technology stacks (Java, Linux, Ruby, Python, .NET, C#, C++)