Specialist Solutions Engineer - Data Science/ML

Summary

Join Databricks as a Specialist Solutions Engineer (SSE) specializing in data science and machine learning solutions. Guide customers in building big data solutions on the Databricks platform, supporting Solution Architects with your Apache Spark™ and other data technology expertise. You will be a customer-facing role, helping customers design and implement essential workloads. As a go-to expert, you'll strengthen your technical skills through mentorship and training, specializing in areas like performance tuning or automation. You will provide technical leadership on big data projects, architect production-level workloads, and become a technical expert in a chosen area. Contribute to the Databricks community through tutorials and training.

Requirements

• 2+ years experience in a customer-facing technical role. Pre-sales or post-sales experience working with external clients across a variety of industry markets
• Data Science/ML Skills
• Experience in a technical role involving the design, implementation, and operationalisation of Machine Learning models in production
• Passion for collaboration, life-long learning, and driving business value through ML
• Hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch
• Experience building production-grade machine learning solutions on AWS, Azure, or GCP
• Experience building Machine Learning solutions on cloud infrastructure and services, such as AWS, Azure, or GCP leveraging a strong understanding of: Model development including building, training, tuning, and evaluation processes
• Different types of ML algorithms and methods, including supervised and unsupervised machine learning, and Deep Learning methods
• MLOps concepts cover model monitoring, tracking, management, model serving & deployment, and other aspects of productionising ML pipelines in distributed data processing environments using tools like MLflow
• Ability to design highly performant, scalable, and cost-effective cloud-based data & ML solutions, such as distributed training and inference processes on GPU clusters
• Experience with big data technologies such as Spark/Delta, Hadoop, NoSQL, MPP, and OLAP
• Deep knowledge of development tools and best practices for engineers including CI/CD, unit and integration testing, and automation and orchestration
• Proven ability to maintain and extending production data systems to evolve with complex needs
• Strong programming experience in Python and potentially Scala/R

Responsibilities

• Provide technical leadership to guide strategic customers to successfully implement big data projects, ranging from architectural design to development best practices, automation, and performance tuning
• Architect production-level workloads, including end-to-end pipeline load performance testing and optimization
• Become a technical expert in an area such as performance tuning, development best practices, automation, or streaming
• Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing, and custom architectures
• Provide tutorials and training to improve community adoption (including hackathons and conference presentations)
• Contribute to the Databricks Community

Preferred Qualifications

Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)

Benefits

• This role can be remote, but we prefer that you be located in the job listing area and can travel up to 40% when needed
• At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees