Full-Time Data Scientist Reinforcement Learning

• You will design and prototype regression‑based pricing models (linear, GLM, mixed‑effects, Gaussian Process, etc.) with a strong Bayesian lens. Implement scalable inference (SVI, MCMC, importance sampling) to handle sparse, high‑volume, or streaming data.
• You will extend multi‑armed‑bandit frameworks with richer priors, contextual signals, and uncertainty quantification.
• You will build reproducible data pipelines for feature engineering, label creation, and automated data quality checks e.g (Airflow/Dagster); Good SQL and clean code principles.
• You will work with package models as modular services (FastAPI, Docker, Kubeflow, or similar).
• You will set up performance dashboards and Bayesian control charts to detect drift, over‑fitting, or revenue anomalies in near real time.
• You will design A/B and multivariate tests, plus quasi‑experimental or causal‑impact methods when randomization isn’t possible. Quantify expected value of information to decide when to explore versus exploit.
• You will pair with product managers and revenue strategists to translate fuzzy ideas into testable hypotheses. Coach peers on modern Bayesian workflows; publish internal tech notes and run hands‑on workshops.

• You have deep statistical chops – 5 + years applying regression, hierarchical models, or state‑space methods in production.
• You have experience with Bayesian toolbelt – fluent with PyMC, Stan, NumPyro, TFP, or equivalent; comfortable writing custom likelihoods and priors.
• You have variational & sampling expertise – proven experience scaling inference with SVI, black‑box VI, or advanced MCMC on large data sets.
• You have solid software engineering skills – Python is home (base knowledge of type hints, testing, CI/CD)
• You have experience with cloud & orchestration – hands‑on with AWS, GCP, or Azure; containerisation (Docker/K8s) and workflow schedulers.
• You have data‑driven storytelling skills – able to frame uncertainty, lift, and risk in plain English for execs while diving deep with scientists & engineers.
• You have growth mindset – you follow the latest in probabilistic ML and love to share what you learn.

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