SmithRx · 8 hours ago

Principal Data Engineer, Machine Learning

United States

Full-time

Remote

Lead/Staff

12+ years exp

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Responsibilities

Develop strategies across the entire AI/ML project lifecycle. This includes seamless integration with data platforms, spanning from problem definition and data preparation to model deployment and performance monitoring.

Design, develop, and deploy machine learning models that drive anomaly detection, and predictive insights, leveraging data from our enterprise data warehouse ecosystems.

Build data pipelines and ensure scalable, efficient ML pipelines and workflows for the entire model lifecycle, from problem definition and data preparation to model deployment and monitoring.

Maintain a focus on data quality, security, and compliance in all AI/ML initiatives, particularly within healthcare.

Provide technical leadership, mentoring, and guidance to team members, establishing and enforcing best practices in data engineering and data science.

Collaborate with product managers, data engineers, and engineer leadership to define use cases and integrate AI models and algorithms into existing systems and applications.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningData EngineeringAI/ML frameworksData ArchitectureDatabase DesignPythonSQLSparkPySparkAirflowDBTSnowflakeCortexOpenAITerraformTensorFlowScikit-learnPyTorchAnomaly DetectionPredictive InsightsData QualityData SecurityComplianceMentoring

Required

BS, MS, or PhD in Computer Science, Information Systems, or a related field, with 12+ years of experience in data engineering, data science, or a similar role.

Strong expertise in data architecture, database design, and optimization, with experience in OLTP, OLAP, NoSQL, and cloud-based data warehouses (e.g., AWS Snowflake, PostgresDB, DymanoDB, etc).

Proficiency in programming languages such as Python, and SQL, and tools like Spark, PySpark, Airflow, DBT, Snowflake, Cortext, OpenAI, and Terraform.

Proficiency in machine learning frameworks and libraries such as TensorFlow, scikit-learn, or PyTorch, with hands-on experience in building and deploying machine learning models in production.

Track records in developing and deploying ML models, preferably within the healthcare or related industries.

Ability to lead cross-functional teams, influence stakeholders, and manage complex projects in a fast-paced environment.

Strong analytical and problem-solving skills, with the ability to handle evolving requirements and ambiguous challenges.

Excellent communication and presentation skills, capable of conveying complex technical concepts to both technical and non-technical audiences.