About the Role:
We are seeking a highly analytical and research-oriented Machine Learning Scientist with deep expertise in Bayesian inference to join our cutting-edge ML research and product development team. You will be responsible for designing, implementing, and applying advanced probabilistic models to solve complex, high-uncertainty problems in forecasting, personalization, experimentation, and decision-making.
You will work alongside a world-class team of engineers, scientists, and product leaders to drive innovation in applied Bayesian machine learning and help shape intelligent systems at scale.
Key Responsibilities:
- Design and implement Bayesian models for uncertainty estimation, time-series forecasting, recommender systems, causal inference, or reinforcement learning
- Develop scalable inference algorithms using techniques such as MCMC, variational inference, and Bayesian deep learning
- Conduct rigorous experiments and model validation, and analyze results using principled probabilistic approaches
- Collaborate with cross-functional teams including product managers, data engineers, and ML researchers to translate business challenges into modeling problems
- Publish high-quality internal documentation and optionally contribute to peer-reviewed research
- Stay up-to-date with the latest academic literature and emerging tools in Bayesian statistics and probabilistic programming
Required Qualifications:
- Master’s or PhD in Machine Learning, Statistics, Applied Mathematics, Computer Science, or related field
- 3+ years of hands-on experience with Bayesian modeling or probabilistic machine learning
- Strong programming skills in Python, including libraries such as NumPy, PyMC, TensorFlow Probability, Stan, Edward, or similar
- Solid understanding of Bayesian theory: prior/posterior estimation, conjugate priors, hierarchical models, etc.
- Proficiency with statistical inference, experimental design, and evaluation metrics
- Ability to communicate complex probabilistic concepts to both technical and non-technical audiences
Preferred Qualifications:
- Experience applying Bayesian methods in real-world settings such as demand forecasting, A/B testing, marketing attribution, or scientific computing
- Familiarity with big data environments and ML pipelines (e.g., Apache Spark, Airflow, MLflow)
- Contributions to research in Bayesian inference, including publications or conference papers
- Exposure to hybrid models combining neural networks with Bayesian inference (e.g., Bayesian neural nets, Gaussian processes)
What We Offer:
- The opportunity to work on state-of-the-art probabilistic ML systems with high-impact business applications
- A collaborative environment with deep technical mentorship and peer learning
- Competitive compensation package including stock options and performance bonuses
- Flexible work arrangements and access to cutting-edge tools and cloud resources
- A culture of continuous learning, scientific rigor, and open innovation
How to Apply:
📩 Send your CV, project portfolio (GitHub, research papers, open-source contributions), and a short statement of interest to: careers-bayesian@[yourcompany].com
Subject: Application – Machine Learning Scientist, Bayesian Inference – [Your Name]
🗓 Applications reviewed on a rolling basis