Description

What makes this a great opportunity?

Suntory Global Spirits is a world leader in premium spirits with $5.5 billion in annual revenues and an ambition to become the World's Most Admired Premium Spirits Company. We have a strong vision and strategy, an incredible brand portfolio grounded in quality and craftsmanship, an unwavering commitment to sustainability and top talent across the organization. We are focused on driving value across key priorities including American whiskey, Japanese Spirits, Scotch, Tequila and Ready-to-Drink. Headquartered in New York City, Suntory Global Spirits is a subsidiary of Suntory Holdings, which is world renowned for delivering quality and excellence across a range of products and categories..

This position will support the company's Digital Technologies & Analytics initiatives by taking a senior technical role in the development of advanced analytics capabilities and innovation. This is an opportunity to help shape the Digital Technologies & Analytics initiatives with growth potential beyond this role.

Role Responsibilities

• Drive the implementation of the defined innovation strategy through the design and architecture for analytics platform components/services utilizing Google Cloud Platform infrastructure and provide technical expertise to deliver advanced analytics.
• Design, build & operate a secure, highly-scalable, reliable platform to consume, integrate and analyze complex data using a variety of best-in-class platforms and tools.
• Collaborate with global IT teams to develop ML application architecture patterns for ML data ingestion, model training pipelines, real-time inference systems, and ML analytics solutions.
• Drive innovation through developing ML-focused proof-of-concepts and prototypes for scalable machine learning productionization and deployment challenges.
• Provide strong technical skills with ability to design ML system architecture, from data preprocessing to model serving, and dive into low-level implementation details of distributed ML systems.
• Proven experience working with globally distributed agile teams managing ML projects across time zones with collaborative development practices.
• Develop and maintain modern MLOps methodology including automated testing for ML models, continuous integration for data pipelines, and infrastructure as code

Qualifications

• A bachelor's degree in computer science, math, physics, engineering or a related field
• 5+ years of progressive experience in MLops and analytics, with at least 3 years' experience in Google cloud platform
• Overall, 9+ years of progressive experience working in software engineering teams (mentoring junior engineers, setting technical direction, etc.).
• Google Cloud Platform: Experience with GCP stack for Data Analytics - BigQuery (DWH concepts, ANSI SQL), DataProc (Spark/Hadoop), Dataflow (Apache Beam/Scala/Python)
• Data Pipeline Development: Build ETL processes, data platforms, data modeling, and data governance for end-to-end ML solutions.
• Understanding of Fast API and messaging queue.
• Understanding of REST APIs.
• Experience building solution on cloud and Databricks.
• Programming Languages: Strong experience in Python with solid foundation in data structures and algorithms
• Development Tools: Proficiency with Eclipse, IntelliJ, and debugging skills for customer-facing ML products.
• Real-time Streaming: Develop real-time data streaming pipelines for ML inference and model training.
• Cloud Architecture: Understanding of private/public cloud design, virtualization, distributed systems, load balancing, networking, massive data storage, Hadoop, MapReduce, and security.
• Kubernetes: Design ML model deployments, implement auto-scaling for ML workloads, manage resource allocation for training/inference.
• Docker: Build optimized container images for ML applications, implement multi-stage builds, manage container registries with security best practices.
• Jenkins & DevOps: Develop CI/CD (continuous integration and Continuous Deployment ) pipelines for ML deployment, assess and build DevOps/Cloud tooling to improve developer experience.
• Large Scale Systems: Expertise in building cloud-based and open source ML projects with hands-on solution-driven approach
• ML Pipeline Development: Design end-to-end ML pipelines using Kubeflow/MLflow/Airflow, ensure reproducible and scalable workflows.
• Production ML Solutions: Experience with model versioning, A/B testing, feature stores, model monitoring, and real-time inference system