At a Glance
- Tasks: Define grading criteria and evaluate AI performance in real-world data science tasks.
- Company: Join a leading AI research organisation focused on innovative data science projects.
- Benefits: Competitive salary, flexible work arrangements, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on continuous improvement and feedback.
- Why this job: Shape the future of AI by ensuring high standards in data science evaluations.
- Qualifications: 5+ years in data science, strong communication skills, and experience with AI evaluation.
The predicted salary is between 61200 - 74800 £ per year.
Mercor is partnering with a leading AI research organization to engage experienced data scientists for a project focused on evaluating how well AI systems perform real-world data science work. Rather than producing deliverables yourself, you will define what excellent work looks like: designing task-specific grading criteria and scoring completed work samples with rigorous, well-reasoned written justifications.
Key Responsibilities
- Design precise, task-specific grading criteria for real-world data science deliverables (analyses, models, dashboards, experiment readouts, and written recommendations).
- Score AI-generated and human work samples against those criteria, with detailed written justifications for every score.
- Apply consistent, evidence-based judgment so that scores are reproducible and defensible.
- Incorporate structured feedback from senior reviewers and iterate quickly on your work.
Ideal Qualifications
- 5+ years of professional data science experience in industry.
- Background in business operations, product, or growth data science at top-tier technology companies.
- Deep fluency in experiment design and A/B testing, metric definition, SQL/Python analysis, and communicating findings to executive stakeholders.
- Exceptionally strong written communication.
- Detail-oriented, consistent, and comfortable having your judgment reviewed and calibrated against peers.
- Prior experience with AI training, evaluation, or human-data projects is a strong plus.
Data Science Expert - AI Evaluation in London employer: Obsidian
Obsidian is an exceptional employer located in the vibrant Greater London area, offering a dynamic work culture that fosters innovation and collaboration among experts in the field. Employees benefit from a fast-start program with opportunities for growth and extension, alongside a commitment to quality in AI model training that makes a meaningful impact in genomics. With a focus on professional development and a supportive environment, Obsidian is dedicated to empowering its team members to excel in their careers.
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Expert - AI Evaluation in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Obsidian!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Science Expert - AI Evaluation at Obsidian.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Obsidian.
✨Apply Directly through Our Website
When you find a suitable opening like Data Science Expert - AI Evaluation at Obsidian, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Science Expert - AI Evaluation in London
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 Obsidian, 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 Obsidian. 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 Obsidian
✨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 Obsidian!
✨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.
