At a Glance
- Tasks: Evaluate AI models and improve their accuracy using data science and machine learning.
- Company: Join a leading organisation in AI research with a focus on innovation.
- Benefits: Earn $150–$200/hr while working fully remote with flexible hours.
- Other info: Collaborate with top professionals in a dynamic and supportive environment.
- Why this job: Make a real impact in AI by enhancing model performance and analytical capabilities.
- Qualifications: 1+ years of experience in tech or finance, strong Python and data analysis skills.
The predicted salary is between 200 - 200 £ per hour.
We are seeking
Data Scientists and Quantitative Professionals with experience at leading organizations to support an AI research project.
You’ll evaluate AI model performance across statistics, machine learning, experimentation, and quantitative reasoning to help improve model accuracy and analytical capabilities.
Key Responsibilities
- Evaluate AI-generated outputs involving data science, statistics, machine learning, and quantitative problems .
- Create challenging prompts, datasets, and reference solutions.
- Identify flawed methodologies, statistical errors, and weaknesses in quantitative reasoning.
- Assess data analysis, experimentation approaches, and data modeling techniques.
- Required Qualifications
- At least
1 year of experience at a leading company in technology, finance, research, or a related field, with some experience within the past
3 years .
- Strong understanding of
Python, statistics, machine learning, and data analysis .
- Experience with
Pandas, data modeling, and experimentation .
- Strong analytical and quantitative reasoning skills.
- Ability to communicate technical and statistical concepts clearly in writing.
- Based in an
English-speaking country .
- Preferred Qualifications
- Experience evaluating statistical models, machine learning outputs, or quantitative methods.
- Strong knowledge of experimental design, data interpretation, and methodological validation.
- Ability to develop rigorous datasets and reference solutions for complex analytical problems.
Compensation
- Fully remote work.
- #J-18808-Ljbffr
Data Scientist (Remote | $150–$200/hr) employer: Synthires
Join a forward-thinking company that values your legal expertise while offering the flexibility of remote work. With competitive compensation and a culture that fosters collaboration across disciplines, you'll have the opportunity to contribute to innovative AI projects that shape the future of legal technology. This role not only allows for meaningful engagement in corporate law but also provides avenues for professional growth in an evolving field.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist (Remote | $150–$200/hr)
✨Get Involved in Data Science Meetups
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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 (Remote | $150–$200/hr) at Synthires.
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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 Synthires.
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We think you need these skills to ace Data Scientist (Remote | $150–$200/hr)
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 Synthires, 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 Synthires. 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 Synthires
✨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 Synthires!
✨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.
