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Information Technology 🏢 Full Time ⭐️ Verified

Senior Data Scientist

Nexus AI Analytics
San Francisco
Salary Estimate
USD 160.000 – USD 210.000
Live Update
18 Mei 2026
Deadline
18 Mei 2027

Job Description

Are you ready to shape the future of predictive intelligence? Nexus AI is seeking a Senior Data Scientist to join our elite core team in San Francisco. We solve complex, high-stakes problems by turning massive datasets into actionable strategic advantages.

You will work alongside world-class researchers and engineers, building scalable machine learning models that impact millions of users globally. We value ownership, rigorous mathematical thinking, and a passion for data-driven storytelling.

Responsibilities

  • Architect and deploy end-to-end machine learning models in a production environment.
  • Collaborate with cross-functional teams to identify key business opportunities through data exploration.
  • Develop robust evaluation metrics to measure model performance and business impact.
  • Mentor junior data scientists and contribute to our internal ML infrastructure.
  • Champion best practices in code quality, documentation, and model reproducibility.
  • Present complex findings to stakeholders using clear, persuasive data visualizations.

Qualifications

  • Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
  • 5+ years of professional experience in data science, predictive modeling, or machine learning.
  • Proficiency in Python and deep experience with libraries like PyTorch, TensorFlow, or Scikit-Learn.
  • Advanced SQL skills and comfort working with distributed computing frameworks (Spark, Dask).
  • Proven track record of deploying models into cloud environments (AWS, GCP, or Azure).
  • Strong understanding of experimental design (A/B testing) and statistical inference.
  • Excellent written and verbal communication skills with the ability to influence leadership.

Required Skills

Python Machine Learning PyTorch SQL AWS Big Data Statistical Modeling Deep Learning

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