22 of 22 Statistical Modelling Jobs in the East of England

Data Scientist. This builds on the knowledge of the Data+ certification and enables you to demonstrate your knowledge in advanced data processing, cleaning, and statistical modelling concepts. You will demonstrate your knowledge of machine learning, industry trends and use of specialised data science applications. You will also apply … mathematical and statistical methods appropriately. Step 5 is not a requirement for our job guarantee as this stage is designed to advance your data career to the next level. Our money back guarantee If after 1 year of passing your formal qualifications, we have been unable to help ...

security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially ...

security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially ...

security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially ...

security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially ...

security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially ...

security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially ...

security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially ...

security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially ...

classification, summarization, sentiment analysis Computer Vision Image labeling, object detection, image captioning MLOps/Deployment Model lifecycle support, pipeline design, monitoring/rollback flows Statistical Modeling Hypothesis testing, regression diagnostics, p-value calculation Data Engineering Data pipeline logic, cleaning steps, schema validation Feature Engineering Variable selection, encoding/normalization ...

classification, summarization, sentiment analysis Computer Vision Image labeling, object detection, image captioning MLOps/Deployment Model lifecycle support, pipeline design, monitoring/rollback flows Statistical Modeling Hypothesis testing, regression diagnostics, p-value calculation Data Engineering Data pipeline logic, cleaning steps, schema validation Feature Engineering Variable selection, encoding/normalization ...

classification, summarization, sentiment analysis Computer Vision Image labeling, object detection, image captioning MLOps/Deployment Model lifecycle support, pipeline design, monitoring/rollback flows Statistical Modeling Hypothesis testing, regression diagnostics, p-value calculation Data Engineering Data pipeline logic, cleaning steps, schema validation Feature Engineering Variable selection, encoding/normalization ...

classification, summarization, sentiment analysis Computer Vision Image labeling, object detection, image captioning MLOps/Deployment Model lifecycle support, pipeline design, monitoring/rollback flows Statistical Modeling Hypothesis testing, regression diagnostics, p-value calculation Data Engineering Data pipeline logic, cleaning steps, schema validation Feature Engineering Variable selection, encoding/normalization ...

/hour Location: Remote Commitment: 20–30 hours/week Role Responsibilities Collaborate with AI researchers to design, train, and validate quantitative and statistical models. Apply advanced mathematical, statistical, and computational methods to enhance model performance and stability. Evaluate and refine algorithmic frameworks to ensure robustness, precision ...

/hour Location: Remote Commitment: 20–30 hours/week Role Responsibilities Collaborate with AI researchers to design, train, and validate quantitative and statistical models. Apply advanced mathematical, statistical, and computational methods to enhance model performance and stability. Evaluate and refine algorithmic frameworks to ensure robustness, precision ...

/hour Location: Remote Commitment: 20–30 hours/week Role Responsibilities Collaborate with AI researchers to design, train, and validate quantitative and statistical models. Apply advanced mathematical, statistical, and computational methods to enhance model performance and stability. Evaluate and refine algorithmic frameworks to ensure robustness, precision ...

/hour Location: Remote Commitment: 20–30 hours/week Role Responsibilities Collaborate with AI researchers to design, train, and validate quantitative and statistical models. Apply advanced mathematical, statistical, and computational methods to enhance model performance and stability. Evaluate and refine algorithmic frameworks to ensure robustness, precision ...

/hour Location: Remote Commitment: 20–30 hours/week Role Responsibilities Collaborate with AI researchers to design, train, and validate quantitative and statistical models. Apply advanced mathematical, statistical, and computational methods to enhance model performance and stability. Evaluate and refine algorithmic frameworks to ensure robustness, precision ...

/hour Location: Remote Commitment: 20–30 hours/week Role Responsibilities Collaborate with AI researchers to design, train, and validate quantitative and statistical models. Apply advanced mathematical, statistical, and computational methods to enhance model performance and stability. Evaluate and refine algorithmic frameworks to ensure robustness, precision ...

/hour Location: Remote Commitment: 20–30 hours/week Role Responsibilities Collaborate with AI researchers to design, train, and validate quantitative and statistical models. Apply advanced mathematical, statistical, and computational methods to enhance model performance and stability. Evaluate and refine algorithmic frameworks to ensure robustness, precision ...

cyclic spectra, EVM, autocorrelation, constellation tracking, etc.) Designing RF data-processing pipelines built around practical hardware constraints (bandwidth, ADC limits, gain stages, timing jitter) Modelling RF front-end behaviour (filters, mixers, LOs, AGC, noise figure) to improve signal integrity and inference accuracy Developing ML and statistical models … Python proficiency for RF data analysis and prototyping (NumPy, SciPy, matplotlib, scikit-learn, PyTorch) Solid understanding of DSP fundamentals (FFT, filtering, modulation, correlation, noise modelling, resampling) Familiarity with SDR frameworks such as GNU Radio, SDRangel, osmoSDR, or SoapySDR Practical understanding of RF hardware chains (antenna filters mixers ...

cyclic spectra, EVM, autocorrelation, constellation tracking, etc.) Designing RF data-processing pipelines built around practical hardware constraints (bandwidth, ADC limits, gain stages, timing jitter) Modelling RF front-end behaviour (filters, mixers, LOs, AGC, noise figure) to improve signal integrity and inference accuracy Developing ML and statistical models … Python proficiency for RF data analysis and prototyping (NumPy, SciPy, matplotlib, scikit-learn, PyTorch) Solid understanding of DSP fundamentals (FFT, filtering, modulation, correlation, noise modelling, resampling) Familiarity with SDR frameworks such as GNU Radio, SDRangel, osmoSDR, or SoapySDR Practical understanding of RF hardware chains (antenna ? filters ? mixers ...