This role is for highly experienced data scientists with an advanced quantitative degree (Master’s or PhD in Data Science, Statistics, Mathematics, Computer Science, or related fields) and at least 5 years of hands-on data science experience with demonstrable business impact. You must be expert in Python for data science (pandas, numpy, scipy, scikit-learn, statsmodels) and proficient in SQL and database operations. Strong knowledge of statistics, machine learning algorithms, and their real-world applications is essential, along with advanced English (C1+) and the ability to write clear, precise technical documentation. Experience with GenAI (LLMs, RAG, prompt engineering, vector databases), MLOps, and modern ML/AI frameworks (TensorFlow, PyTorch, LangChain) is highly valued. You will design complex, computationally intensive data science problems that simulate realistic end-to-end workflows across industries (e.g., telecom, finance, government, e-commerce, healthcare). For each scenario, you will craft deterministic problems that require non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction—problems that cannot be solved manually within reasonable timeframes. You will implement and verify solutions in Python with standard data science libraries, incorporate big-data and scalability considerations, and document clear business contexts, problem statements, and verified correct answers. Your work will be used to evaluate and train advanced AI systems on real-world, full-pipeline data science tasks.
Total Budget
$6,000
Pay per Label
$50/hr
Time Requirement
Less than 20 hrs/week
Duration
3-6 months
Computational data science tasks for AI evaluation
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Required Location
Workload / Schedule
Flexible schedule and hours
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