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Data Annotation For Ai Jobs in Newtown, CT (NOW HIRING)

We're looking for people who: * Act with urgency, accountability, and purpose * Deliver high ... Lead enterprise AI and data strategy and own the architecture roadmap. * Align AI and data ...

Define and maintain technical standards for enterprise data management, analytics platforms, and AI enablement capabilities. * Design and guide datacentric and AIenabled initiatives , supporting the ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * Minimum of 3 years of in ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * Minimum of 3 years of in ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * Minimum of 3 years of in ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * Minimum of 3 years of Counsel ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * Minimum of 3 years of Counsel ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * Minimum of 3 years of Counsel ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * Minimum of 3 years of Counsel ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * Minimum of 3 years of in ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Define the technical architecture, infrastructure, and data requirements for AI initiatives. Develop high-level solution designs that outline AI models, ML pipelines, and system integrations. Provide ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

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Data Annotation For Ai information

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.

What are the key skills and qualifications needed to thrive as a data annotation specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What job categories do people searching Data Annotation For Ai jobs in Newtown, CT look for?

The top searched job categories for Data Annotation For Ai jobs in Newtown, CT are:

What cities near Newtown, CT are hiring for Data Annotation For Ai jobs?

Cities near Newtown, CT with the most Data Annotation For Ai job openings:

Principal AI & Data Architect

Pitney Bowes

Shelton, CT • On-site

Full-time

Posted 26 days ago


Pitney Bowes rating

8.1

Company rating: 8.1 out of 10

Based on 42 frontline employees who took The Breakroom Quiz

153rd of 495 rated machine equipment manufacturers


Job description

We're hiring at Pitney Bowes, where top talent builds meaningful careers and lasting impact. We Move fast, Deliver excellence, and Win together...that's The Pitney Bowes way. Here, how we work matters just as much as what we achieve.
We're looking for people who:
  • Act with urgency, accountability, and purpose
  • Deliver high quality work with consistency and pride
  • Collaborate effectively and elevate those around them
  • Focus on outcomes that drive impact and growth

Job Description:
You Are
A senior technical leader who sets the direction for the organization's AI and data architecture. You build the scalable, secure, and governed foundation that supports analytics, machine learning, and generative AI. You serve as the architecture authority for AI and data platforms and ensure alignment across business priorities, technology strategy, and delivery teams.
You Will
  • Lead enterprise AI and data strategy and own the architecture roadmap.
  • Align AI and data initiatives with business goals and measurable value.
  • Establish standards for scalable and reusable AI and data capabilities.
  • Serve as a trusted advisor to technology and business leaders on AI strategy.
  • Design modern data architecture including lakehouse, mesh, and hybrid models.
  • Define enterprise data models, canonical schemas, metadata strategy, lineage, and integration patterns.
  • Lead the development of a centralized and scalable enterprise data platform.
  • Build AI and ML platform capabilities including MLOps and LLMOps.
  • Enable consistent model lifecycle management from data ingestion through deployment and monitoring.
  • Standardize tooling, frameworks, and infrastructure for AI delivery.
  • Drive adoption of production-grade AI patterns and reduce experimental silos.
  • Define and enforce data governance including ownership, stewardship, quality, MDM, and lifecycle management.
  • Resolve fragmentation and establish a single trusted data foundation.
  • Embed responsible AI practices including transparency, fairness, and explainability.
  • Partner with security and risk teams to protect sensitive data and models and mitigate AI-related risks.
  • Establish auditability and controls for AI systems.
  • Lead architecture governance through reference architectures, patterns, and reusable components.
  • Conduct architecture reviews for major data platforms and AI-enabled applications.
  • Partner with engineering, product, security, and operations teams to support a federated adoption model.
  • Build and mentor a high-performing team of architects and engineers.
  • Drive collaboration through councils, governance forums, and working groups.

You Bring
  • Enterprise experience with 15 or more years in enterprise architecture, data architecture, or AI and ML platforms.
  • Proven success building enterprise-scale data and AI platforms.
  • Experience driving AI adoption from concept to production at scale.
  • Strong background in AWS, Azure, GCP, and distributed systems.
  • Technical depth across lakehouse, data mesh, ETL and ELT, streaming pipelines, model lifecycle management, MLOps, generative AI, LLM integration, metadata, lineage, and cloud-native architectures.
  • Understanding of security and compliance requirements for data and AI systems.
  • Ability to operate at both strategic and hands-on technical levels.
  • Experience establishing enterprise standards and governance.
  • Proven ability to influence senior stakeholders and cross-functional teams.
  • Track record of building high-talent technical teams.

Success Outcomes in the First 12 to 24 Months
  • Enterprise AI and data platform adopted across business units.
  • Clear ownership and governance in place with reduced data fragmentation.
  • Standardized AI delivery lifecycle with measurable improvements in speed and quality.
  • Increased business impact from AI including revenue growth, cost efficiency, and improved decision quality.
  • Strong architecture governance model that drives consistency and reuse.

Key Performance Indicators
Business Impact
  • AI-driven revenue contribution and cost optimization.
  • Adoption of AI and data capabilities across business units.

Platform and Delivery
  • Time required to deploy AI models.
  • Percentage of workloads using the standardized platform.

Data Quality and Governance
  • Percentage of critical data assets with defined ownership.
  • Improvements in data quality scores.

AI Effectiveness
  • Model accuracy, drift reduction, and business outcome metrics.
  • Return on investment for AI projects.

Risk and Compliance
  • Percentage of AI systems under governance.
  • Reduction in data and AI-related risk incidents.

Location:
This is a hybrid role, with 4 days in the Shelton, CT office required. (No relocation assistance offered.)
Sponsorship:
Must be legally authorized to work in the US. Employer will not sponsor position for employment visa status now or in the future (ex. H-1B).
We will:
• Provide the opportunity to grow and develop your career
• Offer an inclusive environment that encourages diverse perspectives and ideas
• Deliver challenging and unique opportunities to contribute to the success of a transforming organization
• Offer comprehensive benefits globally(PB Benefits and Wellbeing Programs)
Pitney Bowes is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard for race, color, sex, religion, national origin, age, disability (mental or physical), veteran status, sexual orientation, gender identity, or any other consideration made unlawful by applicable federal, state, or local laws.
All qualified applicants, including Veterans and Individuals with Disabilities, are encouraged to apply.
All interested individuals must apply online. Individuals with disabilities who cannot apply via our online application should refer to the alternate application options via our Individuals with Disabilities link.

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