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Gradient AIGradient AIUnited States

Principal Data Scientist

Leads high-impact predictive modeling initiatives using deep learning, large language models, and traditional data science methods. The role requires principal-level experience, strong Python and MLOps expertise, and the ability to translate complex modeling work into measurable business value.

Salary not listed
Remote8+ YOEData Science

About the role

Responsibilities

  • Lead the organization’s most complex and high-impact modeling and analytical initiatives.
  • Set modeling strategy across the organization and drive novel work as a recognized technical authority.
  • Combine deep learning, large language models, and traditional data science techniques to create hybrid models.
  • Brainstorm, prototype, validate, deploy, and commercialize models quickly.
  • Work across big data, federated learning, unstructured data, time-series and sequence modeling, GLMs, XGBoost, and Transformers.
  • Communicate data-driven insights to customers, stakeholders, and prospects, translating complex analysis into business impact.
  • Take initiative and spearhead ambiguous projects.
  • Build reusable systems, packages, and frameworks that elevate the team.
  • Establish team standards and practices for model quality and delivery speed.
  • Own long-term model impact, including MLOps pipelines, drift monitoring, KPI and impact monitoring, issue triage, incremental improvements, and technical debt management.

Requirements

  • Bachelor’s degree in Computer Science, Data Science, Biostatistics, Mathematics, or a related field and 8+ years of professional data science experience building predictive models; or a master’s or Ph.D. in one of these fields and 5+ years of professional data science experience building predictive models.
  • Expert-level knowledge of deep learning, machine learning algorithms, and the core Python data science ecosystem.
  • Strong communication and collaboration skills, especially with nontechnical stakeholders and leadership.
  • Deep experience with natural language, medical data, long-tail predictions, or similar problem spaces.
  • Strong familiarity with all phases of the MLOps model lifecycle.
  • Ability to work effectively with Python, Jupyter, and command-line tools.

Nice-to-Haves

  • Fluency with actuarial methods and experience working with actuaries.
  • Familiarity with healthcare and medical data.
  • Familiarity with underwriting and claims, or predicting long-tailed and/or rare events.

Compensation and Benefits

  • Generous stock options.
  • Unlimited vacation days.
  • Flexible schedule supporting work from home.
  • Medical, dental, vision, 401(k), paid parental leave, and other benefits.
  • Opportunities to learn and take on new responsibilities.

Skills

PythonDeep LearningMachine LearningLLMsMLOpsxgboostTransformersglmsfederated learningnatural language processingtime-series modelingjupyter

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