Essential Job Details

• Position Title: Data Labelling Associate
• Location: Nairobi, Kenya
• Employment Type: Contract
• Number of Vacancies: 1
• Salary: KES 25,000 – KES 35,000 Monthly (Estimated based on market research from reputable sources: Glassdoor and PayScale for data annotation and data entry roles in Nairobi’s BPO sector)
• Category/Department: Data Annotation / AI/ML Support
• Reporting To: Project Supervisor / Quality Assurance Lead
• Application Deadline: 15 September 2025

Introduction

An engaging data labelling associate opportunity is now available at Digital Divide Data in Nairobi, Kenya, for a detail-oriented professional ready to contribute to cutting-edge AI and machine learning projects. This contract role involves accurately annotating datasets to support high-quality data solutions for Fortune 500 companies and leading academic institutions. Ideal for individuals with experience in data entry or annotation, the data labelling associate position offers hands-on involvement in data creation, curation, and labeling tasks, with potential growth into quality assurance responsibilities. By joining Digital Divide Data, you’ll play a key part in delivering end-to-end data services that drive innovation in AI/ML, while building skills in a dynamic BPO environment focused on precision and efficiency.

About Digital Divide Data

Digital Divide Data (DDD) is a leading Business Process Outsourcing (BPO) company specializing in machine learning (ML) data solutions and content services for Fortune 500 corporations and top academic institutions worldwide. What sets DDD apart is its expertise in providing comprehensive end-to-end data creation, curation, labeling, and annotation services, scalable to any project size while maintaining guaranteed quality standards. Operating globally, DDD empowers clients in diverse sectors, from technology to research, by delivering precise datasets that fuel AI advancements. The company fosters a collaborative, innovative culture that values accuracy, continuous learning, and professional development, making it an excellent fit for a data labelling associate to thrive and contribute to impactful data initiatives in Nairobi’s growing tech ecosystem.

Key Responsibilities

• Annotate datasets accurately, including images, videos, text, and LiDAR data, following strict project guidelines and client specifications in the data labelling associate role.
• Validate and check work for quality assurance, ensuring data accuracy and consistency to meet high standards expected by clients.
• Utilize various data annotation platforms and software tools proficiently to complete tasks efficiently and meet daily productivity targets.
• Document annotation processes thoroughly, recording any ambiguities, challenges, or issues encountered during labeling activities.
• Collaborate with team members, supervisors, and cross-functional units to maintain shared understanding and consistency across project executions.
• Integrate feedback from training sessions and supervisors, continuously refining annotation quality, speed, and techniques in the data labelling associate role.
• Stay updated on evolving AI/ML annotation tools, methods, and best practices to enhance personal and team performance.
• Manage time effectively, planning and prioritizing tasks to achieve productivity goals and adhere to project deadlines.
• Support quality assurance transitions by reviewing peer work and identifying areas for improvement when needed.
• Contribute to team meetings by sharing insights on annotation challenges and proposing solutions for better workflow efficiency.
• Handle diverse data types, such as agricultural tech imagery or autonomous systems data, adapting to specific project requirements.
• Maintain confidentiality and data security protocols during all annotation and labeling activities.
• Participate in ongoing training to build proficiency in new annotation software and generative AI-related projects.
• Report any tool malfunctions or process gaps to supervisors for timely resolution.
• Assist in onboarding new team members by demonstrating best practices in data labeling techniques.
• Monitor personal output metrics to ensure alignment with project KPIs and quality benchmarks.
• Engage in continuous improvement initiatives, suggesting enhancements to annotation guidelines.
• Ensure all labeled data complies with ethical standards and client privacy requirements.
• Coordinate with project leads to clarify ambiguous instructions before proceeding with annotations.
• Archive completed datasets properly for easy retrieval and future reference.
• Review historical annotation records to learn from past projects and avoid recurring errors.
• Support ad-hoc tasks, such as data cleaning or preliminary labeling for pilot projects.
• Foster a positive team environment by providing constructive feedback during collaborative sessions.
• Track project progress using internal tools to stay aligned with timelines.
• Adapt to shifting priorities, such as urgent client requests or tool updates, without compromising quality.
• Contribute to documentation of standard operating procedures for new annotation types.
• Participate in quality audits, verifying the accuracy of labeled datasets post-submission.
• Leverage basic AI/ML knowledge to understand the impact of annotations on model training.
• Maintain a clean and organized workspace, both physical and digital, for optimal productivity.
• Demonstrate readiness for QA roles by proactively identifying potential errors in datasets.

Qualifications and Skills

• Education: High school diploma or equivalent required; a diploma or certificate in IT, Data Science, or related fields is an added advantage for the data labelling associate role.
• Experience: At least 2 years in data annotation (e.g., Ag Tech, autonomous systems, image/video annotation, LiDAR), data entry, or similar roles; exposure to Generative AI projects is a strong plus.
• Technical Skills: Basic understanding of AI and machine learning concepts to contextualize annotation tasks.
• Proficiency: Strong computer skills and familiarity with annotation software tools for efficient data labeling.
• Attention to Detail: Exceptional precision and consistency in handling datasets to ensure high-quality outputs.
• Time Management: Ability to prioritize tasks and meet deadlines in a fast-paced data labelling associate environment.
• Collaboration: Strong interpersonal skills to work effectively with teams and supervisors.
• Adaptability: Willingness to learn new tools and transition into Quality Assurance (QA) responsibilities as needed.
• Documentation Skills: Proficiency in maintaining clear records and reporting issues accurately.
• Continuous Learning: Eagerness to stay updated on AI/ML trends and annotation best practices.
• Problem-Solving: Ability to identify and flag ambiguities in data or guidelines.
• Communication: Clear verbal and written skills for feedback integration and team collaboration.
• Ethical Awareness: Commitment to data privacy and confidentiality standards.
• Basic Technical Knowledge: Familiarity with data types like images, videos, text, and LiDAR.
• Preferred Skills: Experience with specific annotation platforms or generative AI datasets.
• Work Ethic: Reliability and dedication to achieving productivity goals.
• Multitasking: Capacity to handle multiple projects simultaneously without errors.
• Analytical Thinking: Basic ability to understand how annotations impact AI model performance.

Company Culture and Values

Digital Divide Data (DDD) promotes a collaborative and innovative work culture that values diversity, precision, and professional growth. As an equal-opportunity employer, DDD does not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, or disability status, fostering an inclusive environment where all employees can thrive. The company emphasizes quality assurance and continuous learning, encouraging team members to engage in training and feedback to enhance skills. With a focus on delivering end-to-end data services to global clients, DDD cultivates a dynamic atmosphere that rewards attention to detail, adaptability, and teamwork. Employees are empowered to contribute ideas for process improvements, aligning with the organization’s commitment to excellence in AI/ML data solutions. For a data labelling associate, this culture provides opportunities to grow from annotation tasks into QA roles while contributing to impactful projects in a supportive BPO setting.

How to Apply

Apply now for the Data Labelling Associate position. Submit your application via the official portal by 15 September 2025:

• A detailed CV highlighting your education, experience, and relevant skills.
• A cover letter explaining your interest in the data labelling associate role and how your qualifications align with Digital Divide Data’s mission.
  Apply through the official portal: Submit Application. Only shortlisted candidates will be contacted. Digital Divide Data is an equal opportunity employer, encouraging applications from all qualified individuals, including those from underrepresented groups.

Additional Engagement

View More Job Opportunities
Explore more vacancies at JobKenya.org: Browse verified jobs.

Free WhatsApp Channel for Job Seekers
Get real-time alerts from our WhatsApp Channel for Job Updates: Join now.

Connect with Us
Follow our updates on Twitter: Kenyan Job Finder.
Join our professional network on LinkedIn: The Kenyan Job Finder.

Tags

data labelling associate, Nairobi jobs, AI annotation Kenya, BPO jobs 2025, data entry careers

Extended Content for Depth and Context

The data labelling associate role at Digital Divide Data is a foundational position in the fast-evolving field of AI and machine learning, where precision in data annotation directly influences the success of advanced models. As a data labelling associate, you will be at the heart of creating high-quality labeled datasets that power innovations for Fortune 500 clients and academic leaders. This contract opportunity in Nairobi demands a keen eye for detail, efficient tool usage, and the adaptability to evolve into quality assurance tasks, making it ideal for entry-to-mid-level professionals eager to build a career in data services.

Why Choose This Data Labelling Associate Role?

The data labelling associate position stands out for its role in bridging human expertise with AI technology, offering hands-on experience in a BPO leader like Digital Divide Data. Unlike routine data entry jobs, this role involves annotating diverse datasets—from images in agricultural tech to LiDAR for autonomous systems—providing exposure to cutting-edge applications. With potential for QA growth, it’s a stepping stone to advanced roles in AI/ML data curation, while the contract nature allows flexibility in a dynamic Nairobi tech scene. Joining DDD means contributing to global projects that drive real-world innovation, all within an inclusive, equal-opportunity workplace.

The Role in AI/ML Data Annotation

In the AI/ML ecosystem, the data labelling associate is essential for preparing datasets that train accurate models. You’ll label elements like objects in videos or sentiments in text, ensuring annotations meet client specs for precision and consistency. This work supports sectors like autonomous driving and generative AI, where quality data is paramount. At Digital Divide Data, the data labelling associate collaborates on end-to-end services, from curation to annotation, gaining insights into how labeled data fuels machine learning breakthroughs and academic research.

Key Responsibilities in Detail

As a data labelling associate, data annotation is core: you’ll tag images, transcribe videos, or classify text using specialized tools, adhering to guidelines for accuracy. Quality assurance involves self-reviewing work and flagging issues, preparing for QA transitions. Proficiency with platforms ensures efficient task completion, while documentation captures challenges for team resolution. Collaboration with supervisors maintains project alignment, and integrating feedback from trainings refines your skills. Time management is key to hitting deadlines, and continuous learning keeps you abreast of new annotation methods, enhancing your value in the data labelling associate role.

Technical Proficiency and Tools

The data labelling associate must master annotation software, from basic interfaces for image tagging to advanced ones for LiDAR point clouds. Basic AI/ML knowledge helps understand annotation impacts, like how precise bounding boxes improve object detection models. Exposure to generative AI datasets adds value, as these require nuanced labeling for training creative algorithms. Digital Divide Data provides training, but self-motivation to learn tools like Labelbox or CVAT is crucial for productivity.

Quality Assurance Transition

A unique aspect of the data labelling associate role is readiness for QA duties. You’ll validate annotations for errors, ensuring datasets meet quality thresholds. This involves checking consistency across team outputs and using metrics like inter-annotator agreement. Transitioning to QA builds analytical skills, positioning you for senior roles where you oversee labeling processes and train juniors, advancing within DDD’s data services pipeline.

Collaboration and Team Dynamics

In the data labelling associate role, teamwork is vital: you’ll discuss ambiguities with peers, share best practices in meetings, and align with cross-functional units like project managers. This fosters a shared understanding, reducing errors and boosting efficiency. Feedback loops from supervisors help refine techniques, while your input on tool usability contributes to process improvements, embodying DDD’s collaborative culture.

Continuous Learning and Development

Staying current is non-negotiable for a data labelling associate. You’ll engage with evolving tools, from AI-assisted annotation to new best practices in ethical labeling. DDD supports this through trainings, but personal initiative—reading industry blogs or experimenting with open-source tools—accelerates growth. This learning mindset prepares you for generative AI projects, where annotations train models for content creation.

Time Management and Productivity

Effective planning defines success as a data labelling associate. You’ll prioritize tasks based on deadlines, balancing volume with accuracy to meet KPIs. Tools like time trackers help monitor output, while breaks prevent fatigue-induced errors. In a contract setting, demonstrating reliability through consistent performance can lead to extensions or full-time opportunities.

Ethical Considerations in Data Labelling

The data labelling associate must uphold ethics: maintaining confidentiality for sensitive datasets, avoiding biases in annotations, and flagging privacy issues. DDD’s equal-opportunity ethos extends here, ensuring diverse perspectives in labeling to create unbiased AI models, aligning with global standards for responsible data practices.

A Day in the Life

A typical day as a data labelling associate starts with reviewing guidelines and logging into annotation software. You might spend mornings labeling images for an Ag Tech project, afternoons validating videos for autonomous systems. Breaks include team huddles for feedback, and evenings wrap with documentation and self-QA checks. Urgent tasks or trainings add variety, keeping the role engaging in Nairobi’s vibrant BPO hub.

Why Nairobi Is Ideal

Nairobi’s booming tech and BPO sector makes it perfect for the data labelling associate role. As a Silicon Savannah hub, it offers access to global clients and talent pools, with DDD’s Paramount Plaza location providing modern facilities. The city’s innovation ecosystem supports skill-building, from meetups to online resources, enhancing your career in data annotation.

Preparing for Success

To excel as a data labelling associate, hone attention to detail through practice annotations and learn basic AI concepts via free courses. Build tool proficiency with trials of open-source software, and develop soft skills like communication for team collaboration. A proactive approach to feedback and learning will set you apart, turning this contract into a launchpad for AI/ML careers.

The Broader Impact

As a data labelling associate, your annotations enable AI advancements that solve real problems, from precision farming in Ag Tech to safer autonomous vehicles. At DDD, you contribute to datasets powering Fortune 500 innovations, bridging the digital divide through quality data services. This role’s impact extends to academic research, fostering knowledge that benefits society.

Career Growth Opportunities

The data labelling associate role opens doors to QA, senior annotation, or data curation positions at DDD. With 2+ years’ experience, you can advance to supervisory roles or specialize in generative AI. The BPO industry’s growth in Nairobi provides networking for transitions to full-time tech jobs, leveraging skills in a high-demand field.

Challenges and Solutions

Common challenges for a data labelling associate include repetitive tasks causing fatigue—combat with breaks and goal-setting. Ambiguous guidelines? Flag them early for clarification. Tool glitches? Document and report promptly. These proactive strategies ensure sustained performance and quality.

Industry Trends in Data Annotation

The data labelling associate field is evolving with AI-assisted tools reducing manual work, but human oversight remains crucial for accuracy. Trends like crowdsourcing and ethical AI demand adaptable professionals. Staying informed positions you to contribute to DDD’s innovative services.

Final Thoughts

The data labelling associate role at Digital Divide Data is an entry to AI/ML data services, offering precision-driven work in a global BPO leader. With growth potential, inclusive culture, and impactful projects, it’s ideal for Nairobi talent. Apply by 15 September 2025 to join a team shaping data’s future.

Salary Estimate: KES 25,000–35,000 monthly, estimated based on market research from reputable sources Glassdoor and PayScale for data annotation, labeling, and data entry roles in Nairobi’s BPO sector, reflecting the contract nature, 2 years experience, and entry-level technical requirements.