As a Data Specialist, you will ensure that data used in AI projects is reliable, well-structured, and compliant with governance standards.

You will engage with stakeholders to ensure use case data is usable, trustworthy, and traceable, enabling the development of high-quality AI tools that improve internal efficiency and automate workflows.

You will also establish data standards to improve data governance and data quality for AI projects.

• Job's Responsibilities 1 Acquire and Prepare Internal Data for AI Initiatives
• Engage with stakeholders to identify and collect data from diverse internal sources, including databases, file systems, and application logs.
• Clean and preprocess raw data to remove noise, handle missing values, and ensure consistency.
• Perform data labeling and annotation to create high-quality datasets for training and evaluation.
• Architect, build, and maintain reliable ETL/ELT pipelines to automate the ingestion, cleaning, and transformation of data from diverse internal sources (databases, applications, logs, documents). 2 Co-Management & Ingestion of Knowledge Bases for AI-Powered Tools
• Collaborate with SMEs to manage & ingest Knowledge Bases for AI-Powered Tools.
• Collaborate with LLM engineers to optimize embedding strategies and retrieval performance.
• Monitor and maintain the health, accuracy, and freshness of knowledge bases.
• Implement access controls and data governance policies for sensitive internal information. 3 Own Data Quality, Governance and Compliance Across AI Projects
• Establish and champion data quality standards, metrics, and monitoring dashboards to proactively identify and resolve data issues.
• Develop and implement data validation frameworks, ensuring the fitness of data for training, evaluation, and inference throughout the AI lifecycle.
• Work with legal and compliance teams to ensure AI projects adhere to data privacy regulations.
• Conduct regular audits of data usage and storage to identify and mitigate risks. 4 Support Data Analysis and Insight Generation for Stakeholders
• Develop interactive dashboards and reports using tools like Tableau or Power BI.
• Collaborate with AI engineers to define data requirements and success metrics for models.
• Monitor & present data findings from AI projects to stakeholders in a clear, actionable, and visually compelling manner. 5 Collaborate Cross-Functionally to Enable Data-Driven Decision Making
• Act as the primary data liaison between AI engineers, business stakeholders, and platform teams, translating data requirements and constraints.
• Create and maintain comprehensive data documentation to ensure clarity and reproducibility.
• Train and support business users on data tools, self-service analytics, and data literacy.
• Contribute to the design and implementation of data infrastructure and architecture.

Qualifications and:

Experience: 1.

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related fields 2. 5+ years of data engineering experience for AI projects is preferred. 3.

Proficiency in data manipulation and analysis using tools such as SQL, Python (Pandas, Num Py), and Excel. 4.

Experience: with data visualization platforms such as Tableau,. 5.

Experience: in building and maintaining data pipelines for AI/ML applications is a plus. 6.

Previous AI project experience in data preparation, analysis, or visualization in a business context is preferred.

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At Schneider Electric, our values and behaviors are the foundation for creating a great culture to support business success.

We believe that our IMPACT values – Inclusion, Mastery, Purpose, Action, Curiosity, Teamwork – starts with us. IMPACT is also your invitation to join Schneider Electric where you can contribute to turning sustainability ambition into actions, no matter what role you play.

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This position will be posted until filled.

Schneider Electric aspires to be the most inclusive and caring company in the world, by providing equitable opportunities to everyone, everywhere, and ensuring all employees feel uniquely valued and safe to contribute their best.

We mirror the diversity of the communities in which we operate, and ‘inclusion’ is one of our core values.

We believe our differences make us stronger as a company and as individuals and we are committed to championing inclusivity in everything we do.

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Our Trust Charter is our Code of Conduct and demonstrates our commitment to ethics, safety, sustainability, quality and cybersecurity, underpinning every aspect of our business and our willingness to behave and respond respectfully and in good faith to all our stakeholders.

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It is our policy to provide equal employment and advancement opportunities in the areas of recruiting, hiring, training, transferring, and promoting all qualified individuals regardless of race, religion, color, gender, disability, national origin, ancestry, age, military status, sexual orientation, marital status, or any other legally protected characteristic or conduct.

As a Data Specialist, you will ensure that data used in AI projects is reliable, well-structured, and compliant with governance standards.

You will engage with stakeholders to ensure use case data is usable, trustworthy, and traceable, enabling the development of high-quality AI tools that improve internal efficiency and automate workflows.

You will also establish data standards to improve data governance and data quality for AI projects.

• Job's Responsibilities 1 Acquire and Prepare Internal Data for AI Initiatives
• Engage with stakeholders to identify and collect data from diverse internal sources, including databases, file systems, and application logs.
• Clean and preprocess raw data to remove noise, handle missing values, and ensure consistency.
• Perform data labeling and annotation to create high-quality datasets for training and evaluation.
• Architect, build, and maintain reliable ETL/ELT pipelines to automate the ingestion, cleaning, and transformation of data from diverse internal sources (databases, applications, logs, documents). 2 Co-Management & Ingestion of Knowledge Bases for AI-Powered Tools
• Collaborate with SMEs to manage & ingest Knowledge Bases for AI-Powered Tools.
• Collaborate with LLM engineers to optimize embedding strategies and retrieval performance.
• Monitor and maintain the health, accuracy, and freshness of knowledge bases.
• Implement access controls and data governance policies for sensitive internal information. 3 Own Data Quality, Governance and Compliance Across AI Projects
• Establish and champion data quality standards, metrics, and monitoring dashboards to proactively identify and resolve data issues.
• Develop and implement data validation frameworks, ensuring the fitness of data for training, evaluation, and inference throughout the AI lifecycle.
• Work with legal and compliance teams to ensure AI projects adhere to data privacy regulations.
• Conduct regular audits of data usage and storage to identify and mitigate risks. 4 Support Data Analysis and Insight Generation for Stakeholders
• Develop interactive dashboards and reports using tools like Tableau or Power BI.
• Collaborate with AI engineers to define data requirements and success metrics for models.
• Monitor & present data findings from AI projects to stakeholders in a clear, actionable, and visually compelling manner. 5 Collaborate Cross-Functionally to Enable Data-Driven Decision Making
• Act as the primary data liaison between AI engineers, business stakeholders, and platform teams, translating data requirements and constraints.
• Create and maintain comprehensive data documentation to ensure clarity and reproducibility.
• Train and support business users on data tools, self-service analytics, and data literacy.
• Contribute to the design and implementation of data infrastructure and architecture.

Qualifications and:

Experience: 1.

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related fields 2. 5+ years of data engineering experience for AI projects is preferred. 3.

Proficiency in data manipulation and analysis using tools such as SQL, Python (Pandas, Num Py), and Excel. 4.

Experience: with data visualization platforms such as Tableau,. 5.

Experience: in building and maintaining data pipelines for AI/ML applications is a plus. 6.

Previous AI project experience in data preparation, analysis, or visualization in a business context is preferred.