16 of 16 Remote Bayesian Methods Jobs in the UK

Job Description Summary In this role, you will understand complex and critical business problems, formulates integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to contribute solving unmet medical needs in neuroscience, discover actionable insights and automate process for reducing effort and time for repeated use. Moreover, you will manage the implementation and adherence … Good understanding of clinical study design principles and basic familiarity working with clinical data in a clinical trial (GxP) setting. Strong knowledge and understanding of (multivariate implementations of) statistical methods such as time to event analysis, machine learning, meta-analysis, mixed effect modelling, longitudinal modelling, Bayesian methods, variable selection methods (e.g., lasso, elastic net … random forest), design of clinical trials. Familiarity with statistical and analytical methods for high dimensional data (e.g. imaging, digital, genetics or -omics data). Strong programming skills in R and Python. Demonstrated knowledge of data visualization, exploratory analysis, and predictive modeling. Excellent interpersonal and communication skills (verbal and writing) Ability to develop and deliver clear and concise presentations for More ❯

and data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms (GCP More ❯

tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for uncertainty-aware predictions Stakeholder Collaboration : Demonstrated ability to work directly with stakeholders to scope, iterate, and deliver impactful solutions in More ❯

tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for uncertainty-aware predictions Stakeholder Collaboration : Demonstrated ability to work directly with stakeholders to scope, iterate, and deliver impactful solutions in More ❯

tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for uncertainty-aware predictions Stakeholder Collaboration : Demonstrated ability to work directly with stakeholders to scope, iterate, and deliver impactful solutions in More ❯

tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for uncertainty-aware predictions Stakeholder Collaboration : Demonstrated ability to work directly with stakeholders to scope, iterate, and deliver impactful solutions in More ❯

tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for uncertainty-aware predictions Stakeholder Collaboration : Demonstrated ability to work directly with stakeholders to scope, iterate, and deliver impactful solutions in More ❯

libraries like mplsoccer . Advanced MLOps & Modelling: Deeper experience with the Vertex AI lifecycle (especially Pipelines ) and advanced modelling techniques relevant to football (player valuation, tactical analysis). Bayesian Modelling: Experience with probabilistic programming (e.g., PyMC). Stakeholder Management: Proven success working directly with business stakeholders to define and deliver impactful solutions. What They Offer Work that More ❯

junior team members, leading research projects, and guiding collaborative efforts is a plus. Knowledge of the following machine learning domains is a plus. Generative models leveraging diffusion or Bayesian Flow Networks. Large-scale distributed machine learning training. Knowledge, experience or interest in the following biological domains is a plus. Drug discovery and protein engineering. Understanding of protein More ❯

Experienced with Cloud Security best practices. Hands-on experience with DevOps lifecycle, tools and frameworks. Desirables Knowledge of ML approaches such supervised/unsupervised machine learning, reinforcement learning, Bayesian inference. AWS Certification is a strong benefit. Experience with Google Clouds Big Data tools. Proficient with Kafka is plus but not essential If youre a data visionary with More ❯

Experienced with Cloud Security best practices. Hands-on experience with DevOps lifecycle, tools and frameworks. Desirables Knowledge of ML approaches such supervised/unsupervised machine learning, reinforcement learning, Bayesian inference. AWS Certification is a strong benefit. Experience with Google Clouds Big Data tools. Proficient with Kafka is plus but not essential If youre a data visionary with More ❯

Strong coding skills in Python, with deep familiarity across ML and data libraries (pandas, numpy, scipy, scikit-learn, tensorflow, pytorch). A broad understanding of machine learning and statistical methods, including NLP, deep learning, Bayesian inference, time-series forecasting, and recommendation systems. Hands-on experience with cloud platforms such as AWS, GCP, or Azure, and exposure More ❯

Strong coding skills in Python, with deep familiarity across ML and data libraries (pandas, numpy, scipy, scikit-learn, tensorflow, pytorch). A broad understanding of machine learning and statistical methods, including NLP, deep learning, Bayesian inference, time-series forecasting, and recommendation systems. Hands-on experience with cloud platforms such as AWS, GCP, or Azure, and exposure More ❯

BigQuery for data modelling and warehousing Some examples of our data science work can be found in our engineering blog: Image catergorisation Moving to a A(/B) Bayesian World Scoring adverts quickly but fairly Life Cycle of Our Package Uplift Model What … We're Looking For Experience delivering data-science solutions in a commercial or research (e.g. PhD) environment A strong understanding of Data Science techniques, including ML algorithms and statistical methods Proficient in building ML pipelines with Python An enthusiasm for engaging with non-technical stakeholders and establishing scientific rigour in teams Experience collaborating on Data Science projects and working More ❯

Qualifications Master's or PhD in Computer Science, Machine Learning, Data Science, or related field Good foundational understanding and subsequent practical application of ML techniques, preferably NLPs, LLMs, Bayesian Optimisation and MCMCs Strong proficiency in Python and ML frameworks and tools (e.g. PyTorch, TensorFlow, JAX, Hugging Face, vLLM, MCP, prompt engineering) Excellent communication skills and ability to … control tools (i.e. git, GitHub etc.) Experience with data scrapping (e.g. Beautiful Soup), extraction (e.g. LLMs) and cleaning. Knowledge with uncertainty quantification and probabilistic modelling approaches such as Bayesian optimisation Experience with cloud deployment pipelines (Docker, cloud platforms, AWS, CI/CD) Familiarity with sustainability or environmental data standards and frameworks What We Offer Opportunity to make More ❯

Qualifications Master's or PhD in Computer Science, Machine Learning, Data Science, or related field Good foundational understanding and subsequent practical application of ML techniques, preferably NLPs, LLMs, Bayesian Optimisation and MCMCs Strong proficiency in Python and ML frameworks and tools (e.g. PyTorch, TensorFlow, JAX, Hugging Face, vLLM, MCP, prompt engineering) Excellent communication skills and ability to … control tools (i.e. git, GitHub etc.) Experience with data scrapping (e.g. Beautiful Soup), extraction (e.g. LLMs) and cleaning. Knowledge with uncertainty quantification and probabilistic modelling approaches such as Bayesian optimisation Experience with cloud deployment pipelines (Docker, cloud platforms, AWS, CI/CD) Familiarity with sustainability or environmental data standards and frameworks What We Offer Opportunity to make More ❯