What are the responsibilities and job description for the Data Scientist position at AI Squared?
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.