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- Tasks: Design and create data science problems for AI projects using Python and SQL.
- Company: Mindrift, a leader in innovative AI solutions.
- Benefits: Earn up to $55/hour, work remotely, and enhance your portfolio.
- Why this job: Shape the future of AI while working on exciting, real-world challenges.
- Qualifications: Master's or PhD in a quantitative field and 5+ years of data science experience.
- Other info: Flexible freelance role that fits around your studies or other commitments.
The predicted salary is between 40 - 55 £ per hour.
This opportunity is only for candidates currently residing in the specified country. Your location may affect eligibility and rates. Please submit your resume in English and indicate your level of English proficiency.
At Mindrift, innovation meets opportunity. We believe in using the power of collective intelligence to ethically shape the future of AI.
What We Do
The Mindrift platform connects specialists with AI projects from major tech innovators. Our mission is to unlock the potential of Generative AI by tapping into real-world expertise from across the globe.
About The Role
GenAI models are improving very quickly, and one of our goals is to make them capable of addressing specialized questions and achieving complex reasoning skills. If you join the platform as a Data Science AI Trainer, you'll have the opportunity to collaborate on these projects.
Responsibilities
- Design original computational data science problems that simulate real-world analytical workflows across industries (telecom, finance, government, e-commerce, healthcare).
- Create problems requiring Python programming to solve (using pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn).
- Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks).
- Develop problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction.
- Create deterministic problems with reproducible answers: avoid stochastic elements or require fixed random seeds for exact reproducibility.
- Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency.
- Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations).
- Incorporate big data processing scenarios requiring scalable computational approaches.
- Verify solutions using Python with standard data science libraries and statistical methods.
- Document problem statements clearly with realistic business contexts and provide verified correct answers.
How To Get Started
Simply apply to this post, qualify, and get the chance to contribute to projects aligned with your skills, on your own schedule. From creating training prompts to refining model responses, you'll help shape the future of AI while ensuring technology benefits everyone.
Requirements
- You hold a Master's or PhD Degree in Data Science, Statistics, Mathematics, Computer Science, or related quantitative field.
- You have at least 5 years of hands-on data science experience with proven business impact.
- You have a portfolio of completed projects and publications showcasing real-world problem-solving.
- You are proficient in Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels).
- You are an expert in statistical analysis and machine learning with deep understanding of algorithms, methods, and their practical applications.
- You are proficient in SQL and database operations for data manipulation and analysis.
- You have experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases).
- You have good understanding of MLOps practices and model deployment workflows.
- You possess knowledge of modern frameworks (TensorFlow, PyTorch, LangChain).
- Your level of English is advanced (C1) or above.
- You are ready to learn new methods, able to switch between tasks and topics quickly and sometimes work with challenging, complex guidelines.
Our freelance role is fully remote so, you just need a laptop, internet connection, time available and enthusiasm to take on a challenge.
Benefits
Why this freelance opportunity might be a great fit for you?
- Get paid for your expertise, with rates that can go up to $55/hour depending on your skills, experience, and project needs.
- Take part in a part-time, remote, freelance project that fits around your primary professional or academic commitments.
- Work on advanced AI projects and gain valuable experience that enhances your portfolio.
- Influence how future AI models understand and communicate in your field of expertise.
Freelance Data Science Engineer (Python & SQL) employer: Mindrift
Contact Detail:
Mindrift Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Freelance Data Science Engineer (Python & SQL)
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science field and let them know you're on the lookout for freelance opportunities. You never know who might have a lead or can refer you to someone looking for your skills.
✨Tip Number 2
Show off your portfolio! Make sure your completed projects are easily accessible online. A well-organised portfolio showcasing your problem-solving skills in Python and SQL will definitely catch the eye of potential clients.
✨Tip Number 3
Stay updated with industry trends! Follow relevant blogs, podcasts, and forums to keep your knowledge fresh. This not only helps in interviews but also shows that you're passionate about your field when discussing projects.
✨Tip Number 4
Apply through our website! We make it super easy for you to find roles that match your expertise. Plus, applying directly gives you a better chance to showcase your skills and enthusiasm for the projects we offer.
We think you need these skills to ace Freelance Data Science Engineer (Python & SQL)
Some tips for your application 🫡
Tailor Your Resume: Make sure your resume is tailored to highlight your experience in data science, especially with Python and SQL. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Show Off Your English Skills: Since we need your resume in English, ensure it’s clear and professional. If you’re confident in your English proficiency, let us know! A quick note on your level can go a long way.
Be Specific About Your Experience: When detailing your past work, focus on specific achievements and the impact of your projects. We love seeing how you’ve tackled real-world problems and what results you achieved!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and keep everything organised. Plus, it shows you’re keen to join our team!
How to prepare for a job interview at Mindrift
✨Know Your Stuff
Make sure you brush up on your Python and SQL skills before the interview. Be ready to discuss specific libraries like pandas and scikit-learn, and have examples of your past projects at hand. This will show that you not only understand the theory but can also apply it in real-world scenarios.
✨Showcase Your Problem-Solving Skills
Prepare to talk about how you've designed computational problems in the past. Think about the end-to-end data science pipeline and be ready to explain how you approached challenges like data cleaning, feature engineering, and model validation. Real-world examples will make your answers stand out!
✨Understand the Company’s Mission
Familiarise yourself with Mindrift's goals and how they aim to shape the future of AI. Being able to connect your skills and experiences to their mission will demonstrate your genuine interest in the role and the company. It shows you're not just looking for any job, but this job.
✨Communicate Clearly
Since the role requires advanced English proficiency, practice articulating your thoughts clearly and concisely. Prepare to explain complex concepts in a way that's easy to understand. This will not only help in the interview but is crucial for collaborating on AI projects with diverse teams.