Lead Data Scientist (Foco em Sust)

Jobgether; Brasil; Remote; BRL 260,000 - 380,000; Full time

Benefits offered by this job

Remote work: fully remote

Technical leadership opportunity

Cross-functional collaboration

Cost and efficiency improvements

Job summary

Jobgether is seeking a Lead Data Scientist (Foco em Sust) based in Brazil to provide technical leadership for DS/ML/AI products. You will guide teams, establish operational standards, and promote best practices across AI lifecycle, ML engineering, and MLOps.

This fully remote role collaborates with business stakeholders, engineering, and external partners, aligning priorities with business value. The position focuses on monitoring, observability, incident management, model governance, and

Qualifications

• Bachelor's degree in Technology, Engineering, Mathematics, Statistics, or related field.
• Experience in technical leadership or team management within DS/ML/AI domains.
• Experience supporting and evolving ML models and AI products in production.
• Strong knowledge of MLOps, model lifecycle governance, CI/CD, monitoring, retraining.
• Experience with monitoring, observability, incident management for AI/ML solutions.
• Proficiency in Python and SQL; familiarity with DS/ML ecosystems.
• Experience with Databricks, Snowflake, or equivalent platforms.
• Ability to translate business needs into technical direction; stakeholder engagement.
• Excellent communication, influencing, and negotiation skills.
• Proven ability to manage priorities across teams and suppliers.
• Collaborate with Data Eng., Architecture, Governance, Security, and Data Product teams.
• Advanced or fluent English proficiency.

Responsibilities

• Lead the technical support, maintenance, and evolution of DS/ML/AI products.
• Ensure availability, performance, scalability, security, and continuity of production models and pipelines.
• Oversee the AI/analytics model portfolio with SLA compliance and continuous improvement.
• Provide technical leadership to teams; set priorities and standards.
• Act as a technical reference for DS/ML Engineering and MLOps teams.
• Establish monitoring and observability for data, models, pipelines, and inference services.
• Maintain model quality through monitoring, validation, and retraining.
• Prioritize and negotiate requests across teams and suppliers.
• Coordinate with business and external partners for alignment and value realization.
• Drive improvements to AI environments for efficiency and scalability.
• Encourage collaboration and knowledge sharing across internal and external teams.

Skills

Python

SQL

MLOps

CI/CD

Model monitoring

Databricks

Snowflake

Communication

Leadership

English

Education

Bachelor's degree

Tools

Databricks

Snowflake

CI/CD tooling

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Data Scientist (Foco em Sust) based in Brazil. The Lead Data Scientist will provide technical leadership for the support, evolution, and reliability of Data Science, Machine Learning, and Artificial Intelligence products in a corporate environment. This role is responsible for ensuring the availability, performance, scalability, security, and operational continuity of production models, machine learning pipelines, and analytical applications. You will guide technical teams, establish operational standards, and promote best practices across Data Science, Machine Learning Engineering, and MLOps. The position also focuses on monitoring, observability, incident management, model governance, and continuous improvement throughout the AI lifecycle. Working closely with business stakeholders, engineering teams, and external partners, you will align technical priorities with business needs, delivery timelines, and expected value. This remote opportunity is ideal for an experienced technical leader who combines deep expertise in AI operations with strong communication, negotiation, and cross-functional collaboration skills.

Accountabilities

• Lead the technical support, maintenance, evolution, and reliability of Data Science, Machine Learning, and AI products.
• Ensure the availability, performance, scalability, security, and operational continuity of production models, machine learning pipelines, and analytical applications.
• Oversee the operational management of the AI and analytics model portfolio, ensuring compliance with service-level agreements (SLAs), timely incident resolution, and continuous improvement.
• Provide technical leadership to teams responsible for supporting and evolving data and AI products, establishing priorities, technical direction, and quality standards.
• Serve as a technical reference for Data Science, Machine Learning Engineering, and MLOps teams, promoting operational excellence and consistent engineering practices.
• Establish and enhance monitoring and observability capabilities for data, models, pipelines, and inference services to detect risks, performance degradation, and operational issues proactively.
• Maintain model quality, relevance, and performance through monitoring, validation, retraining, and effective lifecycle governance.
• Prioritize and negotiate requests across relevant teams, balancing stakeholder expectations, risks, deadlines, and business value.
• Act as a liaison between business areas, technical teams, and external providers, ensuring effective communication, alignment, and data-driven decision-making.
• Drive technological improvements and optimize AI environments to increase operational efficiency, scalability, and cost effectiveness.
• Strengthen the technical capabilities of AI and Analytics support functions by encouraging collaboration, knowledge sharing, and operational excellence among internal teams and external partners.
• Collaborate with Data Engineering, Architecture, Governance, Security, and Data Product teams to ensure reliable and sustainable AI operations.

Requirements

• Bachelor's degree in Technology, Engineering, Mathematics, Statistics, or a related field.
• Experience in technical leadership or team management within Data Science, Machine Learning Engineering, Artificial Intelligence, or related disciplines, with responsibility for technical quality, SLA compliance, and delivery outcomes.
• Demonstrated experience supporting, operating, maintaining, and evolving machine learning models, AI products, and analytical applications in corporate production environments.
• Strong knowledge of Machine Learning Engineering, MLOps, and model lifecycle governance, including version control, CI/CD, model monitoring, and retraining processes.
• Experience with monitoring, observability, operational reliability, and incident management for AI and machine learning solutions.
• Proficiency in 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 organizational levels.
• Excellent communication, influencing, and negotiation skills when working with technical teams, business stakeholders, external partners, and executives.
• Proven ability to manage competing priorities and coordinate initiatives across multiple teams and suppliers.
• Experience collaborating with Data Engineering, Architecture, Governance, Security, and Data Product teams.
• Advanced or fluent English proficiency.

Benefits

• Remote work: The position is listed as fully remote.
• Technical leadership opportunity: Lead the operational reliability and ongoing evolution of enterprise AI, Machine Learning, and analytics solutions.
• Cross-functional collaboration: Work with technical teams, business stakeholders, external partners, and executive audiences.
• Technology and innovation: Contribute to improvements in modern AI platforms, MLOps practices, monitoring, observability, and production model governance.
• Operational impact: Help improve solution reliability, scalability, performance, and cost efficiency across the AI ecosystem.