VP Data Science – ML Engineering

Job not on LinkedIn

🔥 0 minutes ago

🇵🇱 Poland – Remote

⏰ Full Time

🔴 Lead

📊 Data Scientist

👻 Ghost score 10%

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Salesforce

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1999

💼 Consulting

📣 Marketing

☁️ SaaS

Consulting • Marketing • SaaS

Salesforce is a leading cloud-based software company that provides a comprehensive customer relationship management (CRM) platform. The platform connects all company data and teams on an integrated, AI-driven system to improve sales, customer service, and marketing efforts. Salesforce offers scalable solutions for businesses of all sizes, including small and medium enterprises, and provides industry-specific solutions to modernize operations, save time, and reduce costs. Salesforce also offers educational resources through Trailhead and a wide selection of apps on AppExchange to extend its CRM capabilities.

📋 Description

• Set the technical direction for a connected enterprise machine learning platform • Ensure the platform is secure, maintains tenant data separation, and operates across multiple cloud regions • Lead senior applied scientists, machine learning engineers, and data engineers • Guide the transition from batch processing to real-time systems • Coach engineers toward higher-level system design and architecture work in an AI-augmented environment • Own the integration of AI tools into the team's daily development cycle • Configure AI tools, feedback loops, and human-review decisions • Safeguard architectural integrity, security best practices, and technical standards • Coach engineers to audit AI-generated code for logic flaws, vulnerabilities, and performance issues

🎯 Requirements

• Long track record building and scaling machine learning systems running in production • Experience setting technical strategy across several teams • Deep hands-on expertise in Amazon Web Services cloud architecture • Expertise in data isolation across tenants • Expertise in distributed systems • Solid knowledge of statistical modeling, causal inference, and experiment design, including A/B testing • Active, hands-on proficiency with AI development tools • Technical expertise to evaluate architectural integrity and make informed decisions about AI-generated output • Experience assessing candidates on prompt formulation, AI output evaluation, and multi-agent workflow orchestration • Experience influencing senior leaders and mentoring principal-level engineers • Experience building real-time feature platforms or recommendation systems • Ability to explain complex AI designs clearly in speech, diagrams, and writing

🏖️ Benefits

• Benefits and resources supporting balance and well-being • AI agents to accelerate impact • Reasonable accommodation during the application or recruiting process • Equal opportunity and non-discrimination protections

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