At a Glance
- Tasks: Design experiments, preprocess data, build models, and collaborate on AI solutions.
- Company: Join a leading AI lab with a focus on innovation and performance.
- Benefits: Flexible hours, competitive pay, and opportunities for professional growth.
- Other info: Dynamic role with potential for increased hours and continuous application review.
- Why this job: Make an impact in the AI field while working with cutting-edge technologies.
- Qualifications: 3+ years in data science, proficient in Python, and familiar with ML frameworks.
The predicted salary is between 45000 - 55000 £ per year.
Mercor is seeking data scientists to support one of the world’s leading AI labs in building robust, high‑performance systems for next‑generation machine learning applications. In this role, you will focus on hands‑on data science tasks, such as designing experiments, gathering and preprocessing data, building and evaluating models, and collaborating closely with engineering teams to deploy production‑ready solutions.
Ideal candidates should be proficient in Python (Jupyter Notebooks), familiar with machine learning frameworks like TensorFlow or PyTorch, and experienced in analyzing large datasets and building predictive models. In addition, you will write, review, and validate prompt‑based questions used to train AI systems.
You are a good fit if you:
- Have over 3+ years of professional experience in data science or applied analytics.
- Are highly skilled in Python and Jupyter notebooks.
- Have experience using libraries including numpy, pandas, scipy, sympy, scikit-learn, torch, tensorflow.
- Have a bachelor's degree in data science, statistics, computer science, or related field in the U.S., Canada, New Zealand, UK or Australia.
- Have a strong background in one or more of the following areas: exploratory data analysis and statistical inference, machine learning workflows and model evaluation, feature engineering/data preprocessing/data wrangling, or A/B testing/experimentation/causal inference.
- Demonstrate excellent verbal and written communication skills.
- Have strong attention to details.
More About the Opportunity
- Commitment: 20–40 hours per week, with potential to scale up to 80 hours.
- Start Date: Early September, with applications reviewed on a rolling basis.
Application Process
- Submit your resume or project portfolio to get started.
- Complete a brief form outlining your technical background and agent usage experience.
- Applications are reviewed continuously, and selected professionals will be contacted with next steps.
Data Scientist - ML Specialist employer: Obsidian
Mercor is an exceptional employer that fosters a collaborative and innovative work culture, making it an ideal place for data scientists passionate about machine learning. With a commitment to employee growth, we offer opportunities to work alongside leading AI experts while engaging in meaningful projects that shape the future of technology. Located in a vibrant tech hub, our team enjoys flexible working hours and a supportive environment that encourages continuous learning and professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist - ML Specialist
✨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 Scientist - ML Specialist 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 Scientist - ML Specialist 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 Scientist - ML Specialist
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.
