Senior Data Scientist
New
B
Bright Vision TechnologiesData science, machine learning
100% Remote (U.S.)Full-TimeSenior
Salary160,000 - 180,000 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonSQLNumpyPyTorchPandasSparkTensorflowRscikit-learnMLOps
Requirements
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a closely related quantitative discipline.
- Five or more years of professional experience developing production-grade machine learning models, predictive analytics solutions, and enterprise data science applications.
- Knowledge of statistics, probability, machine learning algorithms, data structures, data modeling, feature engineering, model evaluation, and end-to-end machine learning lifecycle principles.
- Advanced working knowledge of Python, R, SQL, Scikit-learn, TensorFlow, PyTorch, Pandas, and NumPy.
- Production-level experience designing, training, validating, deploying, and monitoring machine learning models.
- Experience with regression, classification, clustering, forecasting, recommendation systems, and natural language processing applications.
- Experience with relational and NoSQL databases, large-scale datasets, data warehouses, and distributed data processing platforms.
- Experience with Git-based version control, CI/CD, MLOps, model deployment pipelines, code reviews, and collaborative software development.
- Experience deploying machine learning solutions on AWS, Azure, or GCP.
- Preferred: experience with real-time machine learning systems, streaming analytics, Kafka, Spark Streaming, Docker, Kubernetes, Kubeflow, MLflow, or Airflow.
Responsibilities
- Translate business problems into analytical solutions.
- Develop predictive models and machine learning solutions.
- Design, train, and validate machine learning models.
- Deploy machine learning pipelines and solutions.
- Monitor model performance throughout the operational lifecycle.
- Support data-driven decision-making.
- Work with business stakeholders, data engineers, software developers, product managers, and cross-functional teams in an Agile environment.
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