11 of 11 Permanent 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 ...

Desirable Experience Interest in sport and performance analytics Understanding of market pricing or forecasting models Knowledge of Bayesian methods, machine learning, or advanced statistical techniques Experience with optimisation methods or automated decision systems Familiarity with databases (SQL or NoSQL) Experience with additional programming languages What ...

highly collaborative environment. What you’ll be doing: Design, implement, and analyse causal inference experiments, including randomized controlled trials, natural experiments, and quasi-experimental methods Develop and apply conformal prediction frameworks to provide reliable uncertainty estimates for machine learning models Identify and control for confounding variables in observational studies … Advanced degree (MS or PhD) in a quantitative discipline with deep understanding of statistics 3+ years of professional experience applying statistical and causal inference methods to real-world data Demonstrated expertise in experimental design, including randomized controlled trials and observational study methodologies Strong understanding of conformal prediction theory and ...

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 ...

engineers • Some front-end/product sense (you care about shipping usable tools, not just notebooks) • Experience with causal inference, Bayesian methods, or mechanistic simulation ...

/B testing, modeling using Generative AI/ML, and marketing media attribution models. Advanced training or academic focus in non-parametric statistics, resampling methods, or Bayesian approaches for small sample inference Experience applying sequential testing or multi-armed bandit approaches to maximize insights from limited ...

product, typically you'll collaborate with the research team to productionise ideas that are ahead of the published literature, evaluating and integrating established methods or developing your own algorithms, ensuring reliability and maintainability within a complex, integrated codebase. You'll liaise with clients to understand engineering problems that … when to ask for help from domain experts, researchers and engineers You have strong foundations in probabilistic modelling (Gaussian processes, Bayesian methods, uncertainty quantification) You're a confident communicator, comfortable liaising with clients Apply now to find out more about this Machine Learning Engineer (Python TensorFlow ...

engineering—to translate business objectives into robust analytical solutions. Build software using modern technology to enable investing and asset management at scale. Apply Bayesian modelling and probabilistic programming techniques to address uncertainty and improve prediction accuracy. Analyse large-scale datasets to identify key drivers, trends, and early … Requirements: University degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering, Physics, Economics); advanced degree preferred. Strong expertise in statistical modelling, Bayesian inference, and machine learning. Proficient in Python (using libraries such as NumPy, pandas, scikit-learn, PyMC or Stan) Experienced in SQL. Ability ...

firms and telecoms; including Nissan, L'Oréal, Carrefour, WHSmith, Orange, Beiersdorf, BNP Paribas, and Samsung. Our success stems from combining advanced data technologies, agile methods for quick delivery, and dedicated teams of data scientists, data engineers, business consultants, and data analysts. Our 1,800 employees operate in 25 countries … client stakeholders. Advanced Modelling : Proven ability to implement a range of complex models such as time-series forecasting, gradient boosting, clustering, NLP, and Bayesian inference. ML-Ops & Orchestration : Strong experience with MLOps tools for orchestration, experiment tracking, hyper-parameter tuning, and deploying automated model retraining pipelines. Programming ...

market price prediction - you understand basis risk, seasonality, mean-reversion, and regime shifts intuitively You're fluent in multivariate modelling: VAR/VECM, Bayesian hierarchical models, factor models, LSTM/transformer-based temporal architectures - and you can speak clearly to the tradeoffs between them You're rigorous … model that moves a negotiation outcome is worth more than one that wins a Kaggle leaderboard Nice to Have Experience with causal inference methods applied to market dynamics (synthetic control, difference-in-differences, IV) Familiarity with procurement indices (PPI, ISM, commodity spot/futures markets) and how to incorporate ...

working with existing codebases and extending models Experience with data wrangling, transformation, and pipeline preparation Familiarity with MMM techniques and frameworks such as: Bayesian MMM PyMC/PyMC-Marketing Robyn (or equivalent) Experience working with marketing data (media channels, spend, performance metrics) Ability to work independently with … Please apply directly or email with your CV and availability. KEYWORDS Media Mix Modelling, Marketing Mix Modelling, MMM, Python, PyMC, PyMC-Marketing, Robyn, Bayesian Modelling, Econometrics, Marketing Analytics, Adstock, Saturation, Data Science, MMM Contractor ...