101 to 116 of 116 Bayesian Methods Jobs

uncertainty quantification, experimental design, causal inference and Bayesian modeling. Just as essential is commercial instinct: the judgment to design rigorous, workable methods within real product goals and constraints, built through meaningful, hands-on experience shipping at startup speed. You will report to the CTO and work … experience. Strong statistical and probabilistic reasoning , with hands-on fluency in confidence intervals and hypothesis testing, power analysis, experimental design, causal inference, and Bayesian modeling. Experience designing evaluation methodologies - including black-box and counterfactual approaches - and the test datasets behind them, with good judgment on realism and ...

consider yourself a self-starter with a real passion for projects involving innovative methods in health economics and evidence synthesis? Do you love collaborating, moving the ball forward, and rolling up your sleeves? Are you a health economist with a passion for R programming If so – we want … VBA.Modelling Research Experience: You have experience planning, programming and reporting R-based and Excel-based cost-effectiveness simulation models and applied knowledge of statistical methods in health economics. You can evaluate studies to identify key result drivers, assess data or methodological gaps and suggest solutions.Deliverable Creation: You can program ...

risk, lending, or customer lifetime value modelling. Non-linear methods. You have shipped gradient boosting or neural networks on tabular data in production. Bayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work. Time series modelling. You have modelled temporal ...

harness massive compute power and to use state-of-the-art ML techniques – published in recent conferences or developed entirely in-house – as textbook methods won’t beat the competition.ML is integral to develop successful investment management strategies; it is one of the core drivers of our overall performance … learning, non-convex optimisation, Bayesian non-parametrics, NLP or approximate inferenceExcellent reasoning skills and mathematical ability are crucial: off-the-shelf methods don’t always work on our data so you will need to understand how to develop your own modelsStrong programming skills and experience working ...

multi-modal data ingestion and distribution modelling to production-grade visualisation dashboards. Must-have experience: Probabilistic Time Series Forecasting: Experience using probabilistic time series methods (eg, Bayesian, PyMC, Amazon DeepAR) on small or scarce datasets for distribution prediction. Data Lake & DBaaS Integration: Proven ability to build ...

energised by the opportunity to grow.What You’ll DoExperimentationDesign and analyse A/B tests across Viator’s marketplace, applying sound statistical methods to interpret results and support confident decision-makingChampion experimentation best practices: power calculations, guardrail metrics, and multiple testing corrections.Develop your mastery of causal inference and advanced … data science, analytics, or quantitative research role at a data-driven organization; strong product analysts who are actively upskilling in data science methods are encouraged to applyAdvanced SQL skills and hands-on experience querying and manipulating large datasetsProficiency with data visualisation tools (Tableau, Looker or equivalent) Experience with ...

Customer Lifetime Value modelling projects end to end Identify where the modelling stack is holding the business back Frame modelling opportunities and select appropriate methods Keep production models healthy through monitoring and maintenance Deliver incremental model development and research that reshapes model approaches Set technical direction on multi-quarter … bonus Experience shipping gradient boosting or neural networks on tabular data in production is a bonus Experience with hierarchical models, MCMC, or Bayesian updating is a bonus Experience modelling temporal data involving autocorrelation, drift, or seasonality is a bonus Python experience is a bonus Core Competencies Demonstrates ...

bonus Understanding of model cost functions and inductive biases is a bonus Experience with hierarchical models, MCMC, or Bayesian updating is a bonus Experience modelling temporal data involving autocorrelation, drift, or seasonality is a bonus Python experience is a bonus Core Competencies Demonstrates expertise in credit risk … Risk Modelling Customer Lifetime Value Modelling Machine Learning Model Development Causal Estimation Python Programming Hard Skills Probability Statistics Model Evaluation Hierarchical Models MCMC Bayesian Updating Temporal Data Modelling Inductive Biases Automation Research Methodology Soft Skills Critical Thinking Communication Problem Solving Analytical Mindset Collaboration Industry Keywords Lending Credit ...

knowledge sharing across the discipline. What we're looking forExperience as a Data Analyst (or similar role) within consumer-facing product teams Experience applying methods for statistical inference (e.g. Bayesian, Frequentist) and analysing A/B tests Experience in planning and completing analytical tasks on time ...

efficient data transformations using distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems.Mentor junior team members and utilise model explainability methods to drive feature performance improvements.About You5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale business problems.Deep … understanding of frequentist and Bayesian statistics, plus supervised and unsupervised modelling techniques.Proven experience modelling complex interactions (e.g., cluster or network effects).Expertise in model explainability to tune feature engineering and understand feature interactions.Ability to write high-quality, production-grade Python code and collaborate effectively with non-technical ...

data transformations using distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale … business problems. Deep understanding of frequentist and Bayesian statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects). Expertise in model explainability to tune feature engineering and understand feature interactions. Ability to write high-quality, production-grade Python code ...

array of statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)Experience defining phenotypes from electronic health records.Knowledge of Bayesian inference, high dimensional statistics, causal inference.Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerisation)Experience … building and running reproducible pipelines e.g., using WDL, Snakemake, or NextFlow.Experience using cloud computing and trusted research environments e.g. DNAnexus or Verily Workbench.Experience with methods development within statistical geneticsYour PackageWe are committed to providing a transparent, supportive, and rewarding work environment.Compensation & GrowthCompetitive Salary: Salaries are externally benchmarked annually ...

each stage of the consumer’s lifecycle. Experience overseeing experimentation programmes and practices that inform product or business decisions. A good understanding of methods for statistical inference (e.g. Bayesian, Frequentist) and familiarity with predictive analytics techniques is highly beneficial. Working knowledge of common analytics tools used ...

and develop capabilities while mentoring junior statisticians and building client relationships. The role requires strong expertise in Bayesian statistics, population-adjustment methods (MAIC/ML-NMR), ITCs, and survival analyses, with extensive R and Stan/BUGS programming experience. #J-18808-Ljbffr ...

seeking a Robotics Software Engineer for our Online Calibration team to support scaling across our autonomous vehicle fleet. You will turn calibration and estimation methods into reliable, observable and resource-efficient components of the on-vehicle runtime, operating continuously on constrained, hardware-accelerated platforms. Working across calibration, state estimation … closely related robotics domain, including calibration of cameras, LiDAR, IMUs, vehicle parameters or multi-sensor systems. Strong understanding of filtering and estimation methods, such as Kalman-family filters, Bayesian estimation, smoothing, robust estimation or factor-graph optimisation. Strong modern C++ software-engineering skills and proficiency ...

novel safety evaluations in high-impact domains such as CBRN and Cyber to quantify emerging risks.Executing original technical research in AI safety evaluation methods, taking ideas from concept to publication.Shaping the R&D agenda by identifying strategic opportunities to advance safety evaluation methodology across Faculty and the broader ecosystem.Contributing … testing, jailbreaking, indirect prompt injection, and assessing the robustness of model safeguards.Strong foundational skills in experimental design, statistical analysis, and uncertainty quantification, including Bayesian methods.Deep knowledge of language models, generative AI architectures, training methodologies, and safety mitigation techniques.Solid Python proficiency combined with the engineering discipline required ...