Senior Data Scientist
New
100% Remote (Continental United States)Full-TimeSenior
Salary100,000 - 150,000 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonSQLCloud ComputingMachine LearningPyTorchSparkTensorflowRscikit-learnMLOps
Requirements
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, AI, or related quantitative field.
- 6+ years of professional experience in developing production-grade machine learning models and predictive analytics.
- Advanced working knowledge of Python, R, SQL, Scikit-learn, TensorFlow, PyTorch, Pandas, and NumPy.
- Proven experience with relational and NoSQL databases, data warehouses, and distributed platforms like Spark, Hadoop, or Snowflake.
- Strong experience with Git-based version control, CI/CD processes, and MLOps practices.
- Hands-on experience deploying ML solutions on at least one major cloud platform (AWS, Azure, or GCP).
- Strong analytical, debugging, and problem-solving skills to translate insights into business recommendations.
- Experience with model validation, feature engineering, and end-to-end machine learning lifecycle principles.
- Ability to work in a collaborative, cross-functional Agile team environment.
Responsibilities
- Design, build, and continuously refine scalable machine learning models, predictive analytics solutions, and statistical algorithms.
- Author clean, well-documented, and production-ready analytical code following software engineering best practices.
- Develop data processing pipelines for structured, semi-structured, and unstructured data using Python, SQL, and Spark.
- Implement predictive models, recommendation systems, and NLP systems that integrate with enterprise applications.
- Evaluate and optimize model performance, hyperparameter tuning, and inference latency.
- Maintain robust model lifecycle management using platforms such as MLflow, SageMaker, Vertex AI, or Azure Machine Learning.
- Contribute to MLOps pipeline design and deployment automation using tools like Jenkins, GitHub Actions, or Docker.
- Collaborate in an Agile environment on sprint planning, model design reviews, and cross-functional knowledge sharing.
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