Job description

Data Scientist Washington, DC (Hybrid) About the Role: We are looking for a highly motivated Data Scientist with a strong background in applied machine learning and AI to join our growing team. In this role, you will be a key contributor to the development of core AI/ML solutions that power our platform. You will collaborate closely with product and engineering teams, applying state-of-the-art techniques to solve complex challenges, advance our use of large language models (LLMs), and ensure scalable, production-ready solutions. Key Responsibilities: • Leverage 5+ years of experience in data science to design, implement, and optimize machine learning models and pipelines. • Develop, fine-tune, and evaluate large language models (LLMs) for a variety of applications, ensuring accuracy, performance, and robustness. • Collaborate with engineering and product teams to integrate AI/ML solutions into our platform in a scalable and maintainable way. • Conduct applied research, staying current on advances in LLMs, generative AI, and data science methodologies, and translate them into practical solutions. • Build end-to-end workflows, from data exploration and feature engineering to training, validation, deployment, and monitoring in production. • Apply modern containerization and orchestration techniques (e.g., Docker, Kubernetes) to support reproducible experimentation and deployment. • Work with cloud platforms (e.g., Databricks, AWS, GCP, Azure) to manage data pipelines, large-scale training jobs, and distributed systems. • Collaborate across teams to ensure our AI capabilities align with platform goals and business needs. Qualifications: • 5+ years of experience as a Data Scientist or Machine Learning Engineer, with proven success in deploying models to production. • Hands-on experience with large language models (LLMs); fine-tuning experience strongly preferred. • Strong background in Python and ML frameworks such as PyTorch or TensorFlow. • Proficiency in containerization and orchestration technologies (Docker, Kubernetes). • Experience with cloud platforms and ML ecosystems (Databricks, AWS, GCP, Azure). • Familiarity with MLOps best practices, including model deployment, monitoring, and CI/CD for ML. • Strong analytical and problem-solving skills, with the ability to translate research into production-ready solutions. • Excellent communication and collaboration skills, with the ability to work effectively across product, engineering, and leadership teams. • A proactive, self-starter mindset with a passion for applied research and innovation.

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