Job Description: Job Title: Lead Data Scientist Location: Washington, DC (Onsite) Long Term Contract We are now explicitly seeking candidates with demonstrable, hands-on expertise in the Generative AI lifecycle, and also with strong NLP and Deep Learning acumen. Since this shift significantly changes the interview landscape and screening process; I just wanted to align on the weight distribution: Should Traditional ML, Deep Learning, and GenAI concepts be weighted equally, or, given the nature of the new requirements, must we focus the majority of the evaluation on the candidate’s ability on implementing GenAI solutions and enterprise-scale MLOps deployment ? Understanding this is vital as then there will be a need to restructure the interview questions at all levels, including the structure of our hands-on assessment. Role Summary: We are seeking a highly skilled Senior Data Scientist with expertise in Machine Learning, Artificial Intelligence, Large Language Models (LLMs), foundation models, and fine-tuning techniques. In this role, you will architect, build, and deploy advanced ML & AI solutions that power next-generation products across payments, fraud detection, risk decisioning, and financial data enrichment. This position is ideal for someone who is passionate about building enterprise-grade AI & ML applications and applying cutting-edge techniques to real-world financial and payments challenges. Key Responsibilities: Fine-tune or adapt open-source and proprietary models Design, build, and optimize Generative AI, LLM, and multimodal foundation models for enterprise fintech applications. Build high-performance models for NLP, document intelligence, anomaly detection, risk scoring, predictive analytics, and decisioning use cases. Lead experimentation to evaluate model accuracy, scalability, and fairness. Partner with engineering teams to deploy models on cloud-based ML pipelines (Azure, AWS) & data platforms (Databricks & Snowflake) Work with large-scale structured and unstructured datasets across the payments and financial ecosystem. Implement model monitoring, drift detection, and continuous retraining strategies. Evaluate and operationalize new AI technologies, foundation model architectures, responsible AI frameworks, and emerging research.