Company Description

Our focus is to help high growth life sciences companies leverage AI to solve today’s hardest challenges. We are driven to make the world a better place by providing consulting services to build applications to improve care, enhance discoveries, and enable data-driven decisions.

Our team is composed of creative and results-focused individuals who excel at solving real-world problems. Our diverse backgrounds bring technology and expertise from various disciplines including neuroscience, physics, engineering, computational biology, genomics, mathematics, and computer science.

Our culture is vibrant, connected, rooted in our core values statement that: Our work matters. Our clients are our partners. Our work is our reputation. We own our choices. We are always learning. We support and challenge each other.

This role is eligible for flexible hours and remote work.

Job Description

This role is ideal for a results-focused individual who excels at solving problems at the cutting edge of data science and machine learning with strong technical abilities in data engineering. This is a key role in helping manage Engineering and Data Engineering across Mercury Data Science. This successful candidate will be a thought leader in the evolving Data Science and Machine Learning Operations (MLOps) space.

The candidate will consult on customer engagements, providing technical assessment and architecture review of AI/ML solutions to ensure they align with industry best practices and goals of the customer.

Qualifications

• Worked on a productionized AI/ML system - Data Science solutions in production is our true measure of success
• Broad Cloud Experience -  supports clients across all cloud providers including organizations with hybrid on-prem
• Strong Understanding of data stores and their tradeoffs - we utilize a broad range of databases (Relation, Key-Value, Document, Object, Graph)
• Strong understanding of Big Data tooling with the Cloud - we deal with all different data size, small to large, efficient and timely processing is critical to our success
• Strong understanding of DevOps and code best practices - as a Data Science company we believe strongly in automation for everything including our code and deployment
• Good understanding of MLOps and ML best practices - as a leader in Data Science we stay current on the latest developments and believe MLOps is the future
• Strong project/product management experience - Previous experience guiding agile development practices

Tools We Love

• Programming Languages: Python, Javascript
• DevOps: Terraform, Docker BitBucket or similar pipelines
• ML Workflow Tooling: Sagemaker, MLFlow, Kubeflow, DVC, AWS Step Functions
• Data Processing: AWS Glue (Jobs), Spark, Hadoop, Lambda, ECS, Fargate, Data Brew, SageMaker Training Jobs, etc
• Identity and Access Management: AWS, Google, Azure

Additional Information

Nice to have

• Cloud platform certification (AWS Associate Architect, Associate Developer or higher)
• Managed a cross functional team in the delivery of an AI-driven application to a customer
• Delivered an end-to-end data workflow in a production environment
• Understanding of Information Security best practices for applications and company requirements
• Exposure to PII and HIPAA requirements and compliance for data storage and access
• Ability to support and provide guidance to the organization on all things technical, including more typical IT challenges