4 of 4 Anomaly Detection Jobs in the East Midlands

solve real-world problems. Expertise in statistical modelling, predictive analytics, clustering, classification, and regression techniques. Strong background in data mining, pattern recognition, and anomaly detection to improve service delivery. Proficient in Python, R, or other relevant programming languages used for data science. Strong skills ...

solve real-world problems. Expertise in statistical modelling, predictive analytics, clustering, classification, and regression techniques. Strong background in data mining, pattern recognition, and anomaly detection to improve service delivery. Proficient in Python, R, or other relevant programming languages used for data science. Strong skills ...

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 … 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 ...

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 … 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 ...