Specialist Solutions Engineer - Data Science/ML

FEQ126R238

As a Specialist Solutions Engineer (SSE) specializing in data science and machine learning solutions, you will guide customers in building big data solutions on Databricks that span a large variety of use cases. You will be in a customer-facing role, supporting the Solution Architects by applying your hands-on production experience with Apache Spark™ and expertise in other data technologies. SSAs help customers through the design and successful implementation of essential workloads while aligning their technical roadmap to expand the usage of the Databricks Data Intelligence Platform. As a deep go-to-expert reporting to the Senior Manager, Field Engineering, you will continue to strengthen your technical skills through mentorship, learning, and internal training programs and establish yourself in an area of specialisation - whether that be performance tuning, development of best practices, automation, streaming, or more.

The impact you will have:

• 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

What we look for:

• 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

• You will have 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 
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• 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.