▷ (Immediate Start) Bayesian Data Scientist – Advanced AI& Modeling

all.health is at the forefront of revolutionizinghealthcare for millions of patients worldwide. Combining more than20 years of proprietary wearable technology with clinicallyrelevant signals, all.health connects patients and physicians likenever before with continuous, data-driven dialogue. This uniqueposition of daily directed guidance stands to redefine primary carewhile helping people live happier, healthier, and longer. - JobSummary: We’re seeking a Bayesian Data Scientist with deepexpertise in probabilistic modeling and a strong grasp of modern AIadvancements, including foundation models, generative AI, andvariational inference. This role is perfect for someone who thriveson solving complex modeling challenges, optimizing predictionsunder uncertainty, and developing interpretable, high-impact modelsin real-world systems. You will apply state-of-the-art techniquesfrom Bayesian statistics and modern machine learning to buildscalable, efficient, and insightful models—driving real businessimpact. - Location: Remote / Hybrid / [USA-SF, USA-remote,UK-London, UK-remote] - Responsibilities: Translate predictivemodeling problems and business constraints into robust Bayesian orprobabilistic AI solutions. Design and implement reusable librariesof predictive features and probabilistic representations fordiverse ML tasks. Build and optimize tools for scalableprobabilistic inference under memory, latency, and computeconstraints. Apply and innovate on methods like Bayesian neuralnetworks, variational autoencoders, diffusion models, and Gaussianprocesses for modern AI use cases. Collaborate closely withproduct, engineering, and business teams to build end-to-endmodeling solutions. Conduct deep-dive statistical and machinelearning analyses, simulations, and experimental design. Staycurrent with emerging trends in generative modeling, causality,uncertainty quantification, and responsible AI. -Requirements/Qualifications: Strong experience in Bayesianinference and probabilistic modeling: PGMs, HMMs, GPs, MCMC,variational methods, EM algorithms, etc. Proficiency in Python(must) and familiarity with PyMC, NumPyro, TensorFlow Probability,or similar probabilistic programming tools. Hands-on experiencewith classical ML and modern techniques, including deep learning,transformers, diffusion models, and ensemble methods. Solidunderstanding of feature engineering, dimensionality reduction,model construction, validation, and calibration. Experience withuncertainty quantification and performance estimation (e.g.,cross-validation, bootstrapping, Bayesian credible intervals).Familiarity with database and data processing tools (e.g., SQL,MongoDB, Spark, Pandas). Ability to translate ambiguous businessproblems into structured, measurable, and data-driven approaches. -Preferred Qualifications: M.Sc or PhD in Statistics, ElectricalEngineering, Computer Science, Physics, or a related field.Background in generative modeling, Bayesian deep learning,signal/image processing, or graph models. Experience applyingprobabilistic models in real-world applications (e.g.,recommendation systems, anomaly detection, personalized healthcare,etc.). Understanding of modern ML pipelines and MLOps (e.g.,MLFlow, Weights & Biases). Experience with recent trends suchas foundation models, causal inference, or RL with uncertainty.Track record of publishing or presenting work (e.g., NeurIPS, ICML,AISTATS, etc.) is a plus. - What we are looking for:Curiosity-driven and research-oriented mindset, with a pragmaticapproach to real-world constraints. Strong problem-solving skills,especially under uncertainty. Comfortable working independently andcollaboratively across cross-functional teams. Eagerness to stay upto date with the fast-moving AI ecosystem. Excellent communicationskills to articulate complex technical ideas to diverse audiences.The successful candidate’s starting pay will be determined based onjob-related skills, experience, qualifications, work location, andmarket conditions. These ranges may be modified in the future.#J-18808-Ljbffr