Client-Facing ML Deployment Analyst

Hive; San Francisco (CA); On-site; USD 80,000 - 100,000; Full time

Job summary

Deploy is an intelligence infrastructure platform enabling developers and enterprises to access AI models. We are seeking a Business Analyst for the Deploy Models team to bridge ML, product, and client success during deployments.

You will configure projects, manage client engagements, and ensure integration success through hands-on analysis, scripting, and collaboration with engineering and product teams in a fast-growing SF startup.

Qualifications

• Bachelor's or graduate degree in Computer Science or a related field.
• 1–3 years of work experience in a Product Management or Engineering role at a technology company.
• Working knowledge of Python or Node.js.
• Experience in cross-functional environments; enterprise/SaaS experience is a plus.
• Strong problem-solving and business judgment.
• Excellent written and verbal communication skills.

Responsibilities

• Collaborate with engineering teams to ensure successful technical deployments.
• Write scripts to analyze performance of production machine learning models.
• Analyze customer submissions, data, and results to understand desired changes to the ML models.
• Generate APIs for customers by ensuring accuracy of the model configurations.
• Collaborate with product and other stakeholders to meet customer needs and resolve technical issues.
• Collaborate with product and engineering teams as needed for other deployment aspects.
• Maintain awareness of industry best practices for data handling related to your role.
• Adhere to policies and report security or policy violations as needed.

Job description

Deploy is an intelligence infrastructure platform enabling developers and enterprises to access AI models. We are seeking a Business Analyst for the Deploy Models team to bridge ML, product, and client success during deployments.

You will configure projects, manage client engagements, and ensure integration success through hands-on analysis, scripting, and collaboration with engineering and product teams in a fast-growing SF startup.