Data Scientist - ML Ops

Clairvoyant - Pittsburgh, PA

Full-time - Mid Level

Pittsburgh, PA

10,001+ employees

Professional, Scientific, and Technical Services

About the position

As a Data Scientist specializing in MLOps at EXL, you will be instrumental in transforming data into actionable insights through advanced machine learning techniques. Your role will involve designing and developing complex machine learning models to tackle key business challenges, ensuring efficient deployment and maintenance of these models in real-time production environments. You will collaborate with cross-functional teams to integrate models into existing systems and continuously optimize their performance.

Responsibilities

• Design, build, and validate machine learning models tailored to business needs.
• Develop and implement MLOps strategies to manage the full lifecycle of machine learning models.
• Build and manage robust data pipelines to support model training, testing, and deployment.
• Monitor the performance of deployed models in real-time and address any issues related to model drift, degradation, or failures.
• Work closely with cross-functional teams to deliver data-driven solutions and translate complex technical concepts into actionable insights.
• Create and maintain comprehensive documentation for models, pipelines, and MLOps processes.
• Diagnose and resolve issues related to model performance, deployment, and integration.

Requirements

• Bachelor's or master's degree in computer science, Data Science, Engineering, Mathematics, or a related field.
• 6-9 years of experience in data science, with a strong focus on MLOps and productionizing machine learning models.
• Proficiency in Python for data analysis and machine learning.
• Deep understanding of machine learning algorithms, statistical modeling, and model evaluation techniques.
• Very good knowledge of MLOps principles, tools, and practices, including real-time usage and deployment strategies.
• Hands-on experience with MLOps platforms such as MLflow, Kubeflow, TensorFlow Serving, or similar.
• Experience with major cloud providers (AWS, Azure, Google Cloud) for deploying and managing machine learning models.
• Solid understanding of data engineering principles, including ETL processes, data warehousing, and SQL.
• Proficiency in using version control systems such as Git for code management.
• Strong verbal and written communication skills.

Nice-to-haves

• Experience with big data tools and technologies like Hadoop, Spark, or Kafka.
• Familiarity with Docker, Kubernetes, or other containerization and orchestration technologies.
• Knowledge of DevOps methodologies and tools such as Jenkins, Terraform, or CI/CD pipelines.
• Ability to understand and translate business requirements into technical solutions and model designs.

Benefits

• Paid time off