Data Science Architect

Title: Data Science Architect

Role Description: This is a full-time remote role for an Data Science Architect at Birdeye.

Primary Skills: Machine Learning, MLOps, AI/ML, Cloud (Azure/AWS/GCP), CI/CD, Kubernetes, Docker, Python

Roles & Responsibilities:

Design and implement scalable and robust ML infrastructure to support end-to-end machine learning workflows.

Develop and maintain CI/CD pipelines for ML models, ensuring smooth deployment and monitoring in production environments.

Collaborate with data scientists and software engineers to streamline the model development lifecycle, from experimentation to deployment and monitoring.

Implement best practices for version control, testing, and validation of ML models.

Ensure high availability and reliability of ML systems, including performance monitoring and troubleshooting.

Develop automation tools to facilitate data processing, model training, and deployment.

Stay up-to-date with the latest advancements in MLOps and integrate new technologies and practices as needed.

Mentor junior team members and provide guidance on MLOps best practices.

Qualifications:

Bachelor's/Master's degree in Computer Science, Engineering, or a related technical field with 7-10 years of experience.

Experience in designing and implementing ML infrastructure and MLOps pipelines.

Proficiency in cloud platforms such as AWS, Azure, or GCP.

Strong experience with containerization and orchestration tools like Docker and Kubernetes.

Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI.

Solid programming skills in Python and familiarity with other programming languages such as Java or Scala.

Understanding of ML model lifecycle management, including versioning, monitoring, and retraining.

Experience with infrastructure-as-code tools like Terraform or CloudFormation.

Familiarity with data engineering tools and frameworks, such as Apache Spark, Hadoop, or Kafka.

Knowledge of security best practices for ML systems and data privacy regulations.

Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.

Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

Knowledge of data visualization tools and techniques.

Understanding of A/B testing and experimental design.

Strong analytical and troubleshooting skills.

Excellent communication and documentation skills.

Experience with monitoring and logging tools like Prometheus, Grafana, or ELK stack.

Knowledge of serverless architecture and functions-as-a-service (e.g., AWS Lambda).

Familiarity with ethical considerations in AI and machine learning.

Proven ability to mentor and train team members on MLOps practices