Architect - Data Science Platform

Responsibilities :

• Define and own the overall architecture for our data science platform, including data storage, processing, model development, deployment, and monitoring.
• Design scalable and efficient data pipelines and ETL / ELT processes to ingest, transform, and prepare data for analysis and modelling.
• Evaluate and recommend appropriate technologies, tools, and frameworks for data science workflows, considering factors like performance, cost, and maintainability.
• Develop and enforce data governance policies and standards to ensure data quality, security, and compliance.
• Collaborate with data scientists to understand their analytical needs and translate them into architectural requirements.
• Work closely with data engineers and infrastructure teams to implement and maintain the data science infrastructure.
• Design and implement model deployment strategies, including containerization, API development, and integration with production systems.
• Establish monitoring and alerting systems for data pipelines and deployed models to ensure performance and reliability.
• Research and evaluate new data science technologies and methodologies to drive innovation and improve our capabilities.
• Provide technical leadership and guidance to data scientists and other team members on architectural best practices.
• Communicate complex technical concepts and architectural decisions to both technical and non-technical stakeholders.
• Contribute to the development of data science best practices, standards, and documentation.
• Ensure the scalability and performance of data science solutions to handle growing data volumes and computational Must have 11 to 14 years of experience in machine learning.
• Deep understanding of data science methodologies, machine learning algorithms, and statistical modelling techniques.
• Extensive experience with data warehousing, data lake architectures, and big data technologies (e.g., Hadoop, Spark, Kafka, NoSQL databases).
• Proficiency in programming languages commonly used in data science (e.g., Python, R).
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data science services.
• Strong understanding of data governance, data security, and data privacy principles.
• Experience with MLOps practices and tools for model deployment, monitoring, and management.
• Excellent problem-solving, analytical, and critical thinking skills. (ref : hirist.tech)

ref : hirist.tech)