AI / Data Scientist

Location: Hybrid (Seattle / Remote)

Type: Full-time

Reports to: CTO

Overview

We are looking for a passionate AI / Data Scientist to design, build, and deploy machine learning and generative AI solutions that turn complex data into actionable insights. You will work closely with engineers, product managers, and domain experts to develop models, experiments, and pipelines that drive measurable impact across the business.

Key Responsibilities

• Develop, train, and evaluate machine learning and deep learning models (classification, regression, NLP, computer vision, or generative models).
• Build scalable data pipelines for feature engineering, model training, and real-time inference using Python and cloud platforms (AWS, GCP, or Azure).
• Partner with engineering to deploy models into production and monitor performance over time.
• Apply statistical and causal inference techniques to analyze business or operational data.
• Design experiments (A/B tests, quasi-experiments) to measure impact.
• Stay current with advances in LLMs, embeddings, and multimodal AI; prototype solutions using modern frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, etc.).
• Communicate findings through visualizations, dashboards, and presentations to technical and non-technical audiences.

Required Qualifications

• M.S. or Ph.D. in Computer Science, Data Science, Statistics, Applied Mathematics, or related field (or equivalent practical experience).
• 7 + years of experience building and deploying ML or AI models in production.
• Strong Python skills and experience with packages such as NumPy, pandas, scikit-learn, PyTorch, or TensorFlow.
• Experience with LLM fine-tuning, embeddings, or retrieval-augmented generation (RAG).
• Familiarity with SQL and data-processing frameworks (Spark, PySpark, or AWS Glue).
• Solid understanding of probability, statistics, and experiment design.
• Experience with APIs, microservices, and version control (Git).
• Excellent written and verbal communication skills.

Preferred Qualifications

• Exposure to MLOps / data engineering stacks (MLflow, Airflow, SageMaker, Databricks, or Kubeflow).
• Experience working with large, sensitive, or regulated datasets (e.g., healthcare, finance).
• Contributions to open-source AI projects or published research.