Key skills
PythonSQLAIMachine LearningGenAIRAGLangChainPyTorchTensorFlowscikit-learnPandasNumPyMLOpsAnalyticsStatistical Analysis
About this role
Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. This role involves designing computational data science problems and ensuring they simulate real-world analytical workflows across various industries.
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
Requirements:
- 5+ years of hands-on data science experience with proven business impact
- Portfolio of completed projects and publications showcasing real-world problem-solving
- Expert Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels)
- Expert statistical analysis and machine learning - deep understanding of algorithms, methods, and their practical applications
- Expert with SQL and database operations for data manipulation and analysis
- Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases)
- Understanding of MLOps practices and model deployment workflows
- Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain)
- Strong written English (C1+)