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Remote mid-level Senior Data Scientist with popular ML title and metro location increases applicant density.
Requires specialized ML production, big-data, and AWS expertise, limiting cross-industry portability somewhat.
Explicit 5-year requirement plus mandatory ML production, big-data and AWS tech stack enforces strict filters.
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Design, build, and deploy scalable machine learning models into production systems using Python, SQL, ML/DL frameworks, and big data platforms.
Develop and optimize data pipelines and workflows leveraging Spark, Hadoop, Databricks, Airflow, and AWS EMR for automation and efficiency.
Own end-to-end machine learning projects independently, driving experiments, model training, validation, and integration with cloud-based applications on AWS.
Minimum 5 years of experience in Data Science or Applied Machine Learning.
Proficiency in Python, SQL, machine learning libraries (Pandas, Scikit-learn, TensorFlow, PyTorch).
Experience with big data platforms (Hadoop, Spark), Databricks, Airflow, AWS EMR, and AWS cloud services (S3, Lambda, SageMaker, EC2).
Proven ability to deploy ML models into production and optimize query performance and data pipelines.
Experienced senior-level data scientist with strong hands-on expertise in scalable ML model deployment and big data ecosystems.
Comfortable operating end-to-end from data preprocessing and feature engineering to model deployment and cloud integration within cross-functional teams.
Skilled in leveraging cloud-native services and automation tools to ensure efficient, production-grade ML workflows.