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Nestle

Lead Data Scientist – Sustainability Focus

Nestle

. Provide technical leadership for the support, evolution, and reliability of Nestlé Brazil’s Data Science, Machine Learning, and Artificial Intelligence products.

Posted 10/16/2026full-timeRemote • BrazilSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in Data Science, Machine Learning Engineering, and MLOps, ensuring operational reliability and performance of AI products. Capable of leading technical teams, managing model lifecycles, and aligning business needs with technical solutions.

Highest-signal resume keywords
Technical LeadershipMachine Learning EngineeringMLOpsPythonSQL

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data ScienceMachine LearningAI ProductsModel MonitoringIncident ManagementVersion ControlCI/CDModel Lifecycle GovernanceOperational ReliabilityAnalytical Applications
Soft Skills
CommunicationInfluencingNegotiationCollaborationStakeholder Management
Tools & Technologies
DatabricksSnowflakeAnalytics PlatformsMonitoring ToolsObservability Tools
Industry Keywords
Operational ContinuitySLA ComplianceData EngineeringData GovernanceData Security

Tech Stack

Tools & technologies
PythonSQL

About the role

Key responsibilities & impact
  • Provide technical leadership for the support, evolution, and reliability of Nestlé Brazil’s Data Science, Machine Learning, and Artificial Intelligence products.
  • Ensure the availability, performance, scalability, security, and operational continuity of models, Machine Learning pipelines, and analytical applications in production.
  • Take ownership of the operation and support of the analytical and AI model portfolio, ensuring SLAs, incident resolution, and continuous improvement.
  • Provide technical leadership to teams responsible for supporting and evolving data and AI products, defining priorities, technical direction, and quality standards.
  • Serve as a technical reference for Data Science, Machine Learning Engineering, and MLOps teams.
  • Establish and enhance monitoring and observability for data, models, pipelines, and inference services.
  • Ensure model quality, relevance, and performance through monitoring, validation, retraining, and lifecycle governance.
  • Lead demand prioritization and negotiation, aligning expectations, risks, timelines, and solution value.
  • Connect business areas, suppliers, and technical teams, promoting alignment and data-driven decision-making.
  • Contribute to technological advancement, optimization, operational efficiency, scalability, and cost rationalization across AI environments.
  • Promote technical development, collaboration, knowledge sharing, and operational excellence among internal teams and partners.

Requirements

What you’ll need
  • Bachelor’s degree in Technology, Engineering, Mathematics, Statistics, or a related field.
  • Experience providing technical leadership or managing teams in Data Science, Machine Learning Engineering, Artificial Intelligence, or related areas, ensuring technical quality, SLA compliance, and results delivery.
  • Experience supporting, operating, and evolving Machine Learning models, AI products, and analytical applications in corporate environments.
  • Experience in Machine Learning Engineering, MLOps, and production model lifecycle governance, including version control, CI/CD, model monitoring, and retraining.
  • Experience with monitoring, observability, operational reliability, and incident management for AI and Machine Learning solutions.
  • Experience with Python, SQL, and modern Data Science and Machine Learning ecosystems.
  • Experience with Analytics and AI platforms such as Databricks, Snowflake, or equivalent technologies.
  • Ability to translate business needs into technical direction and lead discussions with stakeholders at different levels.
  • Strong communication, influencing, and negotiation skills with technical audiences, business areas, external partners, and executives.
  • Experience managing competing priorities and coordinating initiatives across multiple teams and suppliers.
  • Experience collaborating with Data Engineering, Architecture, Governance, Security, and Data Product teams.
  • Advanced or fluent English.