About The Position

The Sales Data Science & AI Enablement Analyst is an entry‑level role designed for early‑career talent with a background in data science, analytics, or machine learning. This role supports the development, testing, and operationalization of advanced analytics and AI‑enabled solutions for Sales Operations. Under the guidance of senior team members, the analyst contributes to model development, data preparation, feature engineering, and quality assurance, while learning Vertiv’s analytics platforms, engineering standards, and governance practices.

Requirements

  • Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related field.
  • 0-3 years of foundational experience with Python, SQL, data science, and machine learning concepts (coursework, internships, or projects).
  • Familiarity with version control (Git) and data platforms (Snowflake, or similar).
  • Strong analytical mindset with curiosity and willingness to learn enterprise analytics technologies.
  • Demonstrated attention to detail and commitment to producing high‑quality work.
  • Ability to work collaboratively and communicate effectively with technical and non‑technical stakeholders.

Responsibilities

  • Assist in developing, testing, and refining analytics and AI models for Sales use cases.
  • Contribute to exploratory data analysis, feature engineering, and model evaluation.
  • Document model logic, assumptions, and testing results in alignment with team standards.
  • Prepare, clean, and transform datasets using Python, SQL, and Snowflake.
  • Support integration of new data sources into analytical workflows.
  • Build reusable scripts, templates, and basic automation components under supervision.
  • Learn and apply Vertiv’s analytics technology stack, including Python, Git, Linux environments, Snowflake, Matillion, Sigma, and Power BI.
  • Follow engineering standards for version control, documentation, and code quality.
  • Support senior team members in maintaining shared analytical assets, semantic layers, and reusable patterns.
  • Execute validation checks and contribute to analytics quality assurance processes.
  • Identify anomalies or data quality issues and escalate appropriately.
  • Help ensure analytics deliverables meet reliability and governance expectations.
  • Collaborate with Sales analytics teams to understand data requirements and support analytical solution development.
  • Participate in code reviews, working sessions, and technical discussions.
  • Communicate findings clearly and professionally to team members and partners.
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