About the job
We invite you to submit your CV in English, highlighting your proficiency in the language.
At Mindrift, we specialize in linking talented professionals with exciting, project-based opportunities in AI for prestigious tech firms. Our primary focus is on the testing, evaluation, and enhancement of AI systems. Please note that all positions are project-based and not intended as permanent employment.
Key Responsibilities:
- Develop innovative computational data science challenges that replicate real-world analytical tasks across various sectors, including telecom, finance, government, e-commerce, and healthcare.
- Formulate problems solvable through Python programming, utilizing libraries such as Pandas, Numpy, Scipy, Sklearn, Statsmodels, Matplotlib, and Seaborn.
- Ensure that the tasks are computationally demanding and cannot be solved manually in a reasonable timeframe.
- Design problems requiring complex reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction.
- Create deterministic problems with reproducible outcomes, avoiding stochastic elements or mandating fixed random seeds for consistency.
- Base challenges on actual business scenarios, including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency.
- Construct end-to-end problems that encompass the entire data science workflow from data ingestion to deployment considerations.
- Incorporate scenarios that necessitate scalable computational techniques for big data processing.
- Validate solutions using Python and standard data science libraries, employing statistical methods.
- Clearly document problem statements with realistic business contexts, providing verified correct answers.
Candidate Profile:
This opportunity is ideal for data science experts with Python experience who are open to part-time, non-permanent projects. The ideal candidates will possess:
- 5+ years of practical data science experience with demonstrable business impact.
- A portfolio of completed projects and publications that showcase their problem-solving capabilities.
- Proficient Python programming skills for data science (Pandas, Numpy, Scipy, Scikit-learn, Statsmodels).
- Expertise in statistical analysis and machine learning, with a deep understanding of algorithms and practical applications.
- Advanced SQL skills for data manipulation and analysis.
- Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases).
- Familiarity with MLOps practices and workflows for model deployment.
- Knowledge of modern frameworks such as TensorFlow, PyTorch, and LangChain.
- Strong written English skills (C1 or higher).
Application Process:
Apply → Pass qualification(s) → Join a project → Complete tasks → Get compensated.
