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- Tasks: Become a data labelling expert, ensuring accuracy and integrity in AI and ML models.
- Company: Join a forward-thinking company focused on trust and safety in digital content.
- Benefits: Enjoy flexible working options and opportunities for professional growth.
- Why this job: Make a real impact by upholding content authenticity and collaborating with diverse teams.
- Qualifications: Bachelor's degree or equivalent experience; data review skills are a plus.
- Other info: Potential exposure to sensitive content; strong communication skills are essential.
The predicted salary is between 28800 - 48000 £ per year.
The main function of a Data Labeling Analyst is to create and manage data labels while collaborating closely with our operations team to generate insights and optimise the labeling process. You will play a pivotal role in the development of AI and ML models, ensuring the accuracy and relevance across both integrity and support use cases. A key aspect of the role will involve identifying and addressing instances of bad actors or misrepresentation within data sets to uphold the integrity and authenticity of the content. While this role is not expected to engage with graphic content, there is a risk of some exposure.
Responsibilities:
- Become an expert in various integrity and support issues across Operations.
- Create and modify data labels, ensuring compliance with all regulatory and legal requirements.
- Ensure the quality of labelled data sets, ensuring alignment with content policies and/or support processes.
- Partner with team members to improve processes and tools of the team's labelling interfaces.
- Deliver insights as appropriate related to observed trends in data labelling workflows.
- Identify, investigate, and flag instances of bad actors or misrepresentation within data sets, ensuring compliance with standards for integrity and authenticity.
- Collaborate with cross-functional teams (XFNs) to develop strategies for detecting and mitigating bad actor activity or content misrepresentation.
Typical Day in the Role:
- Policy reasoning rationing
- Conducting review/investigation work for a specific tool - annotating on content
- Set number of annotations to be confirmed
- Working with (tech, integrity and product)
- New technology, skills set critical thinking and strong language skills, articulate to Gen AI why its good and/or bad, language is key, have to be able to communicate effectively.
- Can collaborate with stakeholders, take insights and translate to technical teams.
Required Qualifications:
- Bachelor's degree or equivalent work experience in related field(s) (e.g. Computer Science, Information Systems, Intelligence Studies, Cybersecurity, Mathematics, Business Administration, etc.).
- Professional experience in an office setting, working in an autonomous role.
- Experience in data review and/or labelling processes.
- Ability to identify trends within large data sets.
Escalation Specialist employer: Morgan McKinley
Contact Detail:
Morgan McKinley Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Escalation Specialist
✨Tip Number 1
Familiarise yourself with the latest trends in AI and machine learning, especially in data labelling. Understanding how these technologies work will help you articulate your insights during interviews and demonstrate your passion for the field.
✨Tip Number 2
Network with professionals in the industry, particularly those who work in data integrity and support roles. Engaging in conversations can provide you with valuable insights and may even lead to referrals that could boost your chances of landing the job.
✨Tip Number 3
Prepare to discuss specific examples of how you've identified trends or addressed issues in data sets in previous roles. Being able to share concrete experiences will showcase your analytical skills and problem-solving abilities.
✨Tip Number 4
Stay updated on compliance regulations related to data labelling and integrity. Showing that you are knowledgeable about legal requirements will demonstrate your commitment to maintaining high standards in your work.
We think you need these skills to ace Escalation Specialist
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities of a Data Labelling Analyst. Familiarise yourself with data labelling processes and the importance of integrity in AI and ML models.
Tailor Your CV: Highlight relevant experience in data review and labelling processes on your CV. Emphasise any skills related to critical thinking, language proficiency, and collaboration with cross-functional teams.
Craft a Strong Cover Letter: Write a cover letter that showcases your understanding of the role and your passion for maintaining data integrity. Mention specific examples from your past experiences that demonstrate your ability to identify trends and collaborate effectively.
Proofread Your Application: Before submitting, carefully proofread your CV and cover letter for any errors or inconsistencies. A polished application reflects your attention to detail, which is crucial for this role.
How to prepare for a job interview at Morgan McKinley
✨Know Your Data Labelling Basics
Make sure you understand the fundamentals of data labelling and its importance in AI and ML models. Brush up on how to create and manage data labels, as well as the compliance aspects related to regulatory and legal requirements.
✨Showcase Your Analytical Skills
Be prepared to discuss your experience with identifying trends within large data sets. Highlight any specific examples where you've successfully flagged instances of misrepresentation or bad actors in previous roles.
✨Demonstrate Collaboration Abilities
Since this role involves working closely with cross-functional teams, be ready to share examples of how you've collaborated with others in the past. Emphasise your communication skills and how you translate insights to technical teams.
✨Prepare for Scenario-Based Questions
Expect questions that assess your critical thinking and problem-solving abilities. Prepare to discuss hypothetical situations related to integrity and support issues, and how you would approach them to maintain content authenticity.
