Machine Learning Engineering Lead

Key Skills

AWS, AWS Sagemaker, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Dataiku, Effective communication skills, ETL frameworks, Github, Jenkins, Machine learning techniques, MLflow, Python Programming, Snowflake

Job Description

As the ML Engineering Lead, you will be a leader within the Data Science Industrialization team charged with building and automating high quality data science pipelines that power key business applications with advanced analytics/AI/ML. You will be a leader of a global team that defines and maintains ML Ops best practices and deploys and maintains production analytics and data science modeling workflows.

ROLE RESPONSIBILITIES

• Set a vision and provide day-to-day leadership, supervision, and mentorship for a team of individual contributors with functional expertise that includes analytics, data science, ML Ops, and engineering
• Interface with Enterprise Architecture team to operationalize the vision of MLOps enablement
• Build ML engineering capabilities and contribute to the broader talent building framework
• Provide direction for ML engineering research, design, and implementation of best practices, and facilitate related trainings
• Provide strategic and technical input for data science industrialization roadmap
• Provide input on platform evolution, vendor scan, and overall data science industrialization capability roadmap development
• Lead the advancement of at scale MLOps enablement across bespoke analytics
• Lead ML engineering deployments to enable production models across the ML lifecycle including model training, monitoring, and retraining where required
• Lead ML engineering deployments to enable production models across the ML lifecycle including model training, monitoring, and retraining where required
• Lead implementation of CI/CD orchestration for data science pipelines
• Partner with AIDA Data team to integrate developed ML pipelines into enterprise-level analytics data products where appropriate
• Partner with AIDA Platforms team on continuous development and end to end capability integration between OOB platforms and internal engineered components (API registry, ML library / workflow management, enterprise connectors); Performance and resource optimization of managed pipelines and models

BASIC QUALIFICATIONS

• Bachelor’s degree in ML engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
• 7+ years of work experience in data science, analytics, or engineering for a diverse range of projects
• Deep expertise with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker or other data science platforms
• Strong understanding of data science development lifecycle (CRISP)
• Strong hands-on skills in ML engineering and data science (e.g., Python, industrialized ETL software)
• Deep understanding of MLOps principles and tech stack (e.g. MLFlow)
• Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)
• Experience in CI/CD integration (e.g. GitHub, GitHub Actions or Jenkins)
• Highly self-motivated to deliver both independently and with strong team collaboration
• Ability to creatively take on new challenges and work outside comfort zone
• Strong English communication skills (written & verbal)

PREFERRED QUALIFICATIONS

• Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related discipline
• 2-3 years of hands-on experience leading data science or ML engineering teams
• Hands on experience working in Agile teams, processes, and practices
• Experience in solution architecture & design
• Experience in software/product engineering
• Strong hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 9 or 10) platform
• Pharma & Life Science commercial functional knowledge
• Pharma & Life Science commercial data literacy
• Experience with Dataiku Data Science Studio