4 of 4 Statistical Modelling Jobs in the East of England

experiences, and support international expansion. Key Responsibilities Technical Leadership & Data Science Execution Design, build and deploy predictive models, pricing algorithms, and segmentation frameworks. Apply statistical modelling, machine learning and experimentation techniques to real‐world business problems. Use Python, R and SQL for data exploration, feature engineering, modelling ...

across model analysis, client analysis, and advanced methodologies Define and embed best practices for reproducible, modular, and scalable data science pipelines Set standards for statistical validity, analytical rigor, and reproducibility of outputs across the organisation Ensure strong alignment with machine learning, data engineering, and clinical teams Translate ML outputs … science outputs that support commercial offerings and client value propositions Own the transformation of machine learning outputs into clinical trial endpoints, biomarker definitions, and statistical outputs used by sponsors Ensure outputs are scientifically valid, clinically interpretable, and suitable for regulatory use and use by sponsors Develop advanced analytical methodologies ...

looking for from you: Strong and significant software engineering experience (C++, Java or similar) Solid background in data science and machine learning research, or statistical modelling Experience developing and prototyping novel systems or approaches Ability to take research concepts through to practical application An adaptable and continuous learning ...

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 ...