Data Science Expert

Job Description Job description 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. Position Overview We are seeking experienced data scientists to create computationally intensive data science problems for an advanced AI evaluation project. This is a remote, project-based opportunity for experts who can design challenging problems that require computational methods to solve and mirror the full data science lifecycle - from data acquisition and processing to statistical analysis and actionable business insights. What You'll Do 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 Problem Domains (Examples) Business Analytics & Predictive Modeling Customer segmentation, churn prediction, and CLV modeling algorithms Fraud detection with precision-recall optimization A/B test statistical calculations and experimental design Cohort analysis and retention rate computations Model evaluation metrics and performance optimization Statistical Analysis & Data Processing Hypothesis testing with multiple comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast accuracy metrics and moving average computations Optimization & Operations Research Resource allocation and constraint satisfaction problems Cost-benefit analysis and capacity planning optimization Network analysis and supply chain calculations Database & Mathematical Foundations Complex SQL query optimization and window functions Probability computations for business scenarios Linear algebra operations and matrix factorization Information theory metrics (entropy, mutual information) Optimization algorithms and convergence analysis Requirements Education & Core Experience Advanced degree (Master's or PhD) in Data Science, Statistics, Mathematics, Computer Science, or related quantitative field 5+ years of hands-on data science experience with proven business impact Portfolio of completed projects and publications showcasing real-world problem-solving Technical Skills Expert Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels) Statistical analysis and machine learning - deep understanding of algorithms, methods, and their practical applications SQL and database operations for data manipulation and analysis Modern AI/ML (Highly Valued) 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) Professional Skills Ability to design problems reflecting real-world business scenarios and industry challenges Strong analytical and problem-solving skills with attention to detail Experience translating business requirements into technical solutions Clear technical writing and documentation skills Proficient in English for problem creation and communication Preferred Qualifications Cross-industry experience (finance, telecommunications, healthcare, technology) Research background with publications or case studies Teaching, mentoring, or training experience Professional certifications in data science or machine learning Compensation Task-based compensation. Application Process Please submit: CV/Resume highlighting ML research or engineering background Domain knowledge test Python test Interview