At a Glance
- Tasks: Lead a team to ensure high-quality data for AI models and develop innovative QA processes.
- Company: Join TaskUs, a leader in AI data quality and engineering.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with a focus on innovation and leadership in a fast-paced environment.
- Why this job: Make a real impact on AI systems by ensuring data integrity and quality.
- Qualifications: Bachelor's degree in a technical field and 3+ years in data quality management.
The predicted salary is between 36000 - 60000 £ per year.
What can you expect in an AI Data Quality & Engineering Lead role with TaskUs:
Why this role exists:
As AI systems scale rapidly across industries, the integrity and accuracy of testing, training, and evaluation data have never been more critical. TaskUs needs a proactive leader who can architect and uphold high‑quality annotation workflows so that AI models are built and evaluated on reliable data without compromising on speed or efficiency.
The impact you’ll make:
- Build and guide a high-performing team: Lead and mentor a team of Data Quality Analysts, setting clear quality goals, delivering feedback, and fostering a culture of precision and accountability.
- Ensure quality at scale: Develop and continually refine robust QA processes, SOPs, and statistical quality metrics (e.g., F1 score, inter‑annotator agreement) to protect the integrity of annotation outputs.
- Drive transparency and insight: Create dashboards and reports that reveal quality trends, root causes of errors, and improvement opportunities - communicating these insights to leadership and clients.
- Champion tool innovation and efficiency: Manage annotation and QA platforms (like Labelbox, Dataloop, LabelStudio), and lead the evaluation or implementation of new automation tools to elevate efficiency and maintain quality.
Responsibilities:
- Strategic Leadership
- Drive the development, refinement, and documentation of quality assurance processes and standard operating procedures to ensure high-quality outputs.
- Establish comprehensive quality metrics (e.g. F1 score, inter-annotator agreement) that align with business objectives and industry standards.
- Continuously review and refine annotation workflows to proactively identify risks and areas to increase efficiency and reduce errors.
- Act as the subject matter expert on annotation quality, providing ongoing feedback, training, and support to annotators and project teams to uphold the highest quality standards.
- Analysis & Reporting
- Lead in-depth data analysis to diagnose quality issues, assess the effectiveness of quality strategies, and uncover root causes of recurring errors.
- Develop and maintain dashboards that provide real-time insights into quality metrics and project performance.
- Prepare and deliver strategic quality reports to senior management and clients, articulating quality trends, risks, and improvement plans.
- Partner with cross-functional teams, including operational management, engineering, and client services, to align on project goals and quality assurance initiatives.
- Operational Leadership
- Lead a team of Data Quality Analysts and provide mentorship, training, and expertise, fostering a culture of continuous improvement and accountability.
- Manage the configuration and integration of annotation and quality control tools (e.g. Labelbox, Dataloop, LabelStudio), ensuring optimal tool performance and alignment with project requirements
- Identify, evaluate, and implement innovative quality control tools and automation technologies to streamline quality control workflows, enhance analytical capabilities, and improve operational efficiency.
Required Qualifications
- Bachelor’s degree in a technical field (e.g. Computer Science, Data Science) or equivalent professional experience.
- 3+ years of experience in data quality management, data operations, or related roles within AI/ML or data annotation environments.
- Proven track record in designing and executing quality assurance strategies for large-scale, multi-modal data annotation projects.
- Proven track record in a leadership role managing and developing high-performing, remote or distributed teams.
- Deep understanding of data annotation processes, quality assurance methodologies, and statistical quality metrics (e.g., F1 score, inter-annotator agreement).
- Strong data-analysis skills, with the ability to interrogate large datasets, perform statistical analyses, and translate findings into actionable recommendations.
- Excellent communication skills, with experience presenting complex data and quality insights to technical and non-technical stakeholders.
- Proficiency with annotation and QA tools (e.g., Labelbox, Dataloop, LabelStudio).
- High-level of proficiency in common data-analysis tools, such as Excel and Google Sheets.
- Familiarity with programmatic data analysis techniques (e.g. Python, SQL).
- Familiarity with the core concepts of AI/ML pipelines, including data preparation, model training, and evaluation.
Preferred Qualifications
- Prior experience in an agile or fast-paced tech environment with exposure to AI/ML pipelines.
- Experience in a managed services or vendor-driven environment.
- Familiarity with prompt engineering and large-language-model assisted workflows to optimise annotation and validation processes.
- In-depth knowledge of ethical AI practices and compliance frameworks.
Locations
AI Data Quality and Engineering Lead in Hampshire, Portsmouth employer: TaskUs
At TaskUs, we pride ourselves on being an exceptional employer that fosters a culture of innovation and accountability. As an AI Data Quality and Engineering Lead, you will have the opportunity to lead a high-performing team in a dynamic environment, with access to cutting-edge tools and resources that enhance your professional growth. Our commitment to employee development, coupled with a collaborative work culture, ensures that you can make a meaningful impact while enjoying a rewarding career in the heart of the tech industry.
StudySmarter Expert Advice🤫
We think this is how you could land AI Data Quality and Engineering Lead in Hampshire, Portsmouth
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We think you need these skills to ace AI Data Quality and Engineering Lead in Hampshire, Portsmouth
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at TaskUs, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at TaskUs. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at TaskUs
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at TaskUs!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
