Bayesian Data Scientist - Advanced AI & Modeling
- We're seeking a Bayesian Data Scientist with deep expertise in probabilistic modeling and a strong grasp of modern AI advancements, including foundation models , generative AI , and variational inference . This role is perfect for someone who thrives on solving complex modeling challenges, optimizing predictions under uncertainty, and developing interpretable, high-impact models in real-world systems. You will apply state-of-the-art techniques from Bayesian statistics and modern machine learning to build scalable, efficient, and insightful models-driving real business impact.
- Translate predictive modeling problems and business constraints into robust Bayesian or probabilistic AI solutions.
- Design and implement reusable libraries of predictive features and probabilistic representations for diverse ML tasks.
- Build and optimize tools for scalable probabilistic inference under memory, latency, and compute constraints.
- Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases.
- Collaborate closely with product, engineering, and business teams to build end-to-end modeling solutions.
- Conduct deep-dive statistical and machine learning analyses, simulations, and experimental design.
- Stay current with emerging trends in generative modeling, causality, uncertainty quantification, and responsible AI.
- Strong experience in Bayesian inference 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 experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods .
- Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration.
- Experience with uncertainty 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 business problems into structured, measurable, and data-driven approaches.
- M.Sc or PhD in Statistics, Electrical Engineering, Computer Science, Physics, or a related field.
- Background in generative modeling , Bayesian deep learning , signal/image processing , or graph models .
- Experience applying probabilistic 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 such as foundation models , causal inference , or RL with uncertainty .
- Track record of publishing or presenting work (e.g., NeurIPS, ICML, AISTATS, etc.) is a plus.
- Curiosity-driven and research-oriented mindset, with a pragmatic approach to real-world constraints.
- Strong problem-solving skills, especially under uncertainty.
- Comfortable working independently and collaboratively across cross-functional teams.
- Eagerness to stay up to date with the fast-moving AI ecosystem.
- Excellent communication skills to articulate complex technical ideas to diverse audiences.
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