17 of 17 Bayesian Methods Jobs

least 3 data or ML products from problem definition to production deployment and monitoring.* Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics.* Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale.* Proficiency in SQL, Python, and familiarity with ...

least 3 data or ML products from problem definition to production deployment and monitoring. Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics. Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale. Proficiency in SQL, Python, and familiarity with ...

technical and non-technical stakeholders, translate complex model outputs into trading insights and strategic recommendations. Stay current with developments, and state-of-the-art methods in ML/optimisation relevant to energy trading. Your Profile MSc or PhD in Data Science, Statistics, Mathematics, Physics, Computer Science, Operations Research … Python (pandas, NumPy, scikit-learn, LightGBM/XGBoost, or similar); SQL and cloud-based data platforms. Experience with reinforcement learning, Bayesian methods, or time-series deep learning (LSTMs, Transformers) in a trading context. Strong experience with optimisation techniques (LP/MILP, stochastic optimisation) applied to asset ...

technical and non-technical stakeholders, translate complex model outputs into trading insights and strategic recommendations. Stay current with developments, and state-of-the-art methods in ML/optimisation relevant to energy trading. Your Profile MSc or PhD in Data Science, Statistics, Mathematics, Physics, Computer Science, Operations Research … Python (pandas, NumPy, scikit-learn, LightGBM/XGBoost, or similar); SQL and cloud-based data platforms. Experience with reinforcement learning, Bayesian methods, or time-series deep learning (LSTMs, Transformers) in a trading context. Strong experience with optimisation techniques (LP/MILP, stochastic optimisation) applied to asset ...

technical and non-technical stakeholders, translate complex model outputs into trading insights and strategic recommendations. Stay current with developments, and state-of-the-art methods in ML/optimisation relevant to energy trading. Your Profile MSc or PhD in Data Science, Statistics, Mathematics, Physics, Computer Science, Operations Research … Python (pandas, NumPy, scikit-learn, LightGBM/XGBoost, or similar); SQL and cloud-based data platforms. Experience with reinforcement learning, Bayesian methods, or time-series deep learning (LSTMs, Transformers) in a trading context. Strong experience with optimisation techniques (LP/MILP, stochastic optimisation) applied to asset ...

technical and non-technical stakeholders, translate complex model outputs into trading insights and strategic recommendations. Stay current with developments, and state-of-the-art methods in ML/optimisation relevant to energy trading. Your Profile MSc or PhD in Data Science, Statistics, Mathematics, Physics, Computer Science, Operations Research … Python (pandas, NumPy, scikit-learn, LightGBM/XGBoost, or similar); SQL and cloud-based data platforms. Experience with reinforcement learning, Bayesian methods, or time-series deep learning (LSTMs, Transformers) in a trading context. Strong experience with optimisation techniques (LP/MILP, stochastic optimisation) applied to asset ...

technical and non-technical stakeholders, translate complex model outputs into trading insights and strategic recommendations. Stay current with developments, and state-of-the-art methods in ML/optimisation relevant to energy trading. Your Profile MSc or PhD in Data Science, Statistics, Mathematics, Physics, Computer Science, Operations Research … Python (pandas, NumPy, scikit-learn, LightGBM/XGBoost, or similar); SQL and cloud-based data platforms. Experience with reinforcement learning, Bayesian methods, or time-series deep learning (LSTMs, Transformers) in a trading context. Strong experience with optimisation techniques (LP/MILP, stochastic optimisation) applied to asset ...

recommendations. Data Scientists do this by listening to team members, understanding context and challenging business ideas. Data Scientists use diverse techniques - frequentist and Bayesian statistics, machine learning, exploratory and explanatory data analysis, causal inference, data visualization, monte carlo modelling, econometric analysis, etc. Such broad requirements call ...

real world, messy data and delivering production ready solutions. Familiarity with cloud environments such as AWS is beneficial. Exposure to Bayesian methods, random forests, or marketing analytics is advantageous but not essential. Ability to demonstrate how your work has delivered tangible value or influenced decision making. ...

data science projects and demonstrating commercial impact. Familiarity with cloud environments such as AWS is beneficial. Exposure to Bayesian methods, random forests, or similar approaches is highly desirable. Marketing or channel performance experience is helpful but not essential; technical capability is the priority. What They Offer ...

through to multinationals, helping them secure highly sought after skill sets. Key words: Data Scientist, Algorithm Developer, Equity Research, Investments, Quantitative Research, Bayesian Statistics, Mathematics, Computer Science, Analytical Algorithms, Python. C++, Monte Carlo. ...

diverse range of sources, ensuring quality and accuracy prior to use in model creation. Apply statistical and machine learning techniques such as regression, Bayesian modelling, or time-series analysis to improve our UK marketing effectiveness model. Apply learnings from the UK to developing a new model ...

Rust, Python and others). Confident operating at senior leadership level and influencing across functions. Desirable: Google Cloud Platform including BigQuery & AI Platform. Bayesian statistics. Agent based simulation and reinforcement learning The interview journey: 1st stage interview with two engineers from the team. Tech challenge ...

they do with their clients Helping shape new projects based on client needs Contributing to the wider team Improving how they do MMM—tools, methods, automation Sharing knowledge and contributing to internal initiatives Living our values: Curiosity, Creativity, Excellence, Transmission, and Pleasure What our client is looking for: Your … tools like Python, R, SQL, Excel, and data visualisation platforms Experience working with large datasets and building robust models Good understanding of regression, Bayesian approaches, and advanced analytics Able to explain complex results in a simple, business-friendly way How you work Excellent communication, storytelling and presentation ...

observability, reproducibility and monitoring of ML/RL solutions. Infrastructure Ownership: Own the development of analytical frameworks, including A/B testing (using Bayesian Inference and contextual multi-armed bandits techniques) and other data science tooling. Ensuring scalability, accuracy, and reliability across projects. Product & Engineering Collaboration: Coordinate … product and engineering to ensure end-to-end solution success. What we need from you Expertise in clustering, predictive modelling, reinforcement learning, and Bayesian statistics. PHD or MSc or equivalent experience in Data Science, Computer Science, Satistics, Physics or related field 5+ years of Data Science experience ...

researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here 'AI' is interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics; 'Science' covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine are included … clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts are not suitable for generic AI research with general application: candidates ...

researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here 'AI' is interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics; 'Science' covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine are included … clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts are not suitable for generic AI research with general application: candidates ...