11 of 11 Anomaly Detection Jobs in the 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 ...

Security Cloud Engineer, CISSP, CISM, or similar. Collaboration experience with MS Teams\Zoom collaboration. Identity AI adoption for risk-based access analytics and behavioural anomaly detection. Should this position be of interest please submit your CV and I will be in contact as required. Guidant, Carbon60, Lorien ...

cloud governance and FinOps tools to improve cost visibility and control Forecasting & Budgeting – Produce accurate cost projections and align cloud spend with financial planning Anomaly Detection – Identify and respond to cost anomalies to prevent unexpected budget spikes Reporting & Documentation – Prepare clear and comprehensive reports on cloud financial ...

cloud governance and FinOps tools to improve cost visibility and control Forecasting & Budgeting – Produce accurate cost projections and align cloud spend with financial planning Anomaly Detection – Identify and respond to cost anomalies to prevent unexpected budget spikes Reporting & Documentation – Prepare clear and comprehensive reports on cloud financial ...

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 ...

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 ...

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 ...