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DayOne Data Centers

Data, Analytics & AI, Lead

Reposted 4 Days Ago
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In-Office
Singapore, SGP
Expert/Leader
In-Office
Singapore, SGP
Expert/Leader
Lead development and execution of company-wide data, analytics, and AI strategy. Build and manage multidisciplinary teams, govern data quality and compliance, oversee architecture and production ML/AI deployments, select and maintain data platforms and tooling, partner with business stakeholders to deliver high-impact use cases, and measure ROI while driving innovation and adoption.
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Join DayOne – Shaping the Future of Data Infrastructure
DayOne is a global leader in the development and operation of high-performance data centers. As one of the fastest-growing companies in the industry, we’ve built a robust presence across Asia and Europe — and we’re just getting started.
As we expand into new international markets, we’re looking for talented, driven individuals to join us on this exciting journey. This is more than a job — it’s an opportunity to be a key contributor to our dynamic team and help shape the future of global data infrastructure.
If you're passionate about innovation, technology, and growth, we invite you to be part of DayOne’s next chapter.

a.Define and Implement Data, Analytics & AI Strategy

Vision and Roadmap:

  • Develop a Data, Analytics & AI strategy aligned with the company’s business objectives, in collaboration with management and business teams.

  • Prioritize initiatives based on their business impact (e.g. globalization, new products, process optimization, and customer experience).

Data Governance:

  • Establish governance policies (quality, security, accessibility) for Data and Analytics tailored to the company’s size.

  • Implement processes to ensure compliance (e.g. GDPR, industry standards).

b.Leadership and Management of Data, Analytics & AI Teams

Hands-On Technical Leadership

  • Stay close to architecture and key system decisions

  • Review critical designs and technical proposals

  • Step into complex technical problems when needed

  • Prototype or validate high-impact ideas

  • Lead by example in technical depth and problem solving

Team Structuring:

  • Build and lead a multidisciplinary team (data scientists, data engineers, analysts) or work with external resources (freelancers, consulting firms).

  • Define roles and responsibilities for effective project execution.

Skill Development:

  • Train and support teams on best practices in Data, Analytics & AI.

  • Promote a data-driven culture within the

c.Development and Deployment of Data, Analytics & AI Solutions

Design and Industrialization:

  • Oversee the development of Data, Analytics & AI solutions (dashboards, predictive models, automations).

  • Industrialize solutions for scalable and reliable deployment (using tools like Dataiku, Databricks, or cloud solutions).

Collaboration with Business Teams:

  • Provide ongoing support to users to maximize tool adoption.

  • Work closely with business teams to understand their needs and translate them into technical solutions.

d.Management of Infrastructure and ToolsData, Analytics & AI Architecture:
  • Evaluate and deploy tools and platforms suited to the company’s needs (e.g. Microsoft Fabrice, Power BI).

  • Oversee the maintenance and evolution of data infrastructure (data lakes, pipelines, databases).

Security and Compliance:

  • Ensure data security and compliance with

  • Implement regular audits to identify and correct

e.Value Creation and Impact Measurement

Business-driven Use Cases:

  • Lead high-impact Data, Analytics & AI projects (e.g., customer personalization, predictive maintenance, cost optimization).

  • Measure and communicate the ROI of initiatives to justify

Innovation and Technological Watch:

  • Monitor Data, Analytics & AI trends (e.g., generative AI, automation) and assess their relevance to the company.

  • Experiments with new solutions to stay

f.Stakeholder Relations

Internal and External Communication:

  • Present the Data, Analytics & AI strategy and achievements to management and business

  • Collaborate with external partners (technology providers, startups) to accelerate innovation.

Expectation Management:

  • Align stakeholder expectations with the actual capabilities of the team and

  • Prioritize projects based on available

WHAT WE’RE LOOKING FOR:

Experience:

  • 10+ years in software engineering, data, or ML environments

  • Experience leading technical teams in a startup or product-driven B2B SaaS company

  • Track record building and scaling production AI/ML systems

  • Experience with distributed systems, real-time data, and cloud infrastructure.

  • Integration experience with ERP (SAP S/4 HANA) and HRIS (Workday) is a strong plus

  • Background in data center industry, or data-heavy SaaS is a strong plus

  • Previous experience working in Big4 consulting, global enterprises, or fortune 500 is a plus

Technical Depth

  • Strong backend and system architecture expertise (e.g. Microsoft Fabric, PowerBI)

  • Experience deploying ML/AI systems into production, not just research

  • Deep understanding of MLOps, data platforms, and model lifecycle management

  • Comfortable operating across engineering, data science, and analytics domains

  • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field

Leadership & Culture

  • First-principles thinker who breaks down complex problems clearly

  • High ownership and accountability

  • Builder mindset with strong technical judgment

  • Clear communicator across technical and business teams

  • Focused on scalable effectiveness and real outcomes

DayOne is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
If you're ready to grow with one of the fastest-moving companies in the data center industry, apply now and be part of our global journey.

HQ

DayOne Data Centers Singapore, Singapore, SGP Office

Singapore, Singapore

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