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Data Science 🏢 Full Time ⭐️ Verified

Senior Data Scientist, ML & Applied AI

NovaMetrics AI
San Francisco, CA
Salary Estimate
USD 150.000 – USD 190.000
Posting Time
6 Mei 2026
Deadline
6 Mei 2027

Job description

NovaMetrics AI is redefining data-driven decision making for industry-leading clients. We are seeking a Senior Data Scientist to join our San Francisco team and lead end-to-end ML initiatives—from problem framing to production deployment and monitoring.

In this role, you will combine strong technical depth with a product mindset, collaborating with engineering, product, and business stakeholders to translate complex problems into scalable data solutions.

Responsibility

  • Lead end-to-end data science projects from problem definition to deployment in production environments.
  • Design, implement, and optimize scalable machine learning models and data pipelines with a focus on reliability and observability.
  • Partner with product managers, engineers, and data engineers to translate business goals into data-centric solutions.
  • Define success metrics, run rigorous experiments, and maintain reproducible, well-documented workflows.
  • Mentor junior data scientists, perform code reviews, and promote best practices in modelling and experimentation.
  • Communicate results and strategic insights to both technical and non-technical stakeholders.
  • Contribute to data governance, feature store strategy, and ML Ops initiatives to scale impact.

Qualification

  • Master's or PhD in Computer Science, Statistics, Mathematics, or a related field.
  • 5+ years of hands-on data science experience with production-grade ML models.
  • Proficiency in Python (pandas, numpy, scikit-learn) and SQL; experience with TensorFlow or PyTorch.
  • Strong experience with cloud platforms (AWS or GCP) and building scalable data pipelines (Airflow, Spark).
  • Proven track record deploying models to production and building monitoring/alerting for model drift.
  • Excellent communication skills and ability to influence stakeholders across disciplines.
  • Experience with MLOps concepts, feature stores, versioning, and reproducible research practices.

Required Skills

Python SQL Machine Learning Deep Learning Pandas NumPy scikit-learn TensorFlow PyTorch AWS GCP Airflow Spark Data Visualization Feature Store MLOps Model Deployment Experimentation Data Wrangling

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