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Data Science 🏒 Full Time ⭐️ Verified

Senior Data Scientist

NovaMetrics AI
San Francisco
Salary Estimate
USD 120.000 – USD 180.000
Posting Time
5 Mei 2026
Deadline
5 Mei 2027

Job description

Join NovaMetrics AI, a fast-growing data science firm focused on deploying real-world ML solutions.

We are seeking a Senior Data Scientist who thrives in a collaborative, fast-paced environment and is passionate about turning data into strategic impact.

As a member of our analytics team, you will design and deploy models, collaborate with product and engineering teams, and drive data-driven decisions across the company.

Location: San Francisco, with hybrid options.

Responsibility

  • Design, implement, and productionize scalable machine learning models and features.
  • Collaborate with product, engineering, and business stakeholders to translate problems into data-driven solutions.
  • Build robust data pipelines, feature stores, and experimentation frameworks.
  • Evaluate models with rigorous metrics and conduct thorough validation before deployment.
  • Communicate insights through compelling visualizations and dashboards to non-technical audiences.
  • Mentor junior team members and contribute to data science best practices and governance.

Qualification

  • Master’s or PhD in Computer Science, Statistics, Mathematics, or a related field; or equivalent industry experience.
  • 5+ years of hands-on data science experience in production ML systems.
  • Strong programming skills in Python and SQL; experience with R is a plus.
  • Deep understanding of ML algorithms, feature engineering, model evaluation, and deployment patterns.
  • Experience with cloud platforms (AWS, GCP, Azure) and ML pipelines (Airflow, Kubeflow, MLflow).
  • Excellent communication and collaboration skills; ability to translate complex results into business impact.
  • Knowledge of data privacy and governance is a plus.

Required Skills

Python SQL R scikit-learn TensorFlow PyTorch Spark MLflow Airflow AWS GCP Azure data visualization feature engineering model deployment

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