# Data Scientist at AI Squared

> United States · Unspecified · Analytics / Data science · open. Source: https://alderhq.app/jobs/greenhouse%3Aaisquared%3A4604009006 (read 2026-11-14).

## Facts (observed)

- Company: [AI Squared](https://alderhq.app/companies/ai-squared)
- Location text: Washington, DC; HQ
- Where it can be done from (inferred): United States (evidence: "Location: Washington, DC; HQ")
- Posted: 2025-09-24 (416 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/aisquared/jobs/4604009006
- Skills listed (inferred): Python, AWS, GCP, Azure, Kubernetes, Docker, PyTorch, TensorFlow, LLMs, Distributed systems

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Washington DC, n 38): p25 $98k, median $119k, p75 $131k

## Description (the employer's text)

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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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aaisquared%3A4604009006 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.