[8BE] Senior Data Scientist (Statistical Modeling)

We're seeking a Senior Data Scientist with deep expertise in probabilistic AI and statistical machine learning to support a client's e-commerce platform. In this role, you'll design and validate probabilistic models — covering dynamic pricing, shipping cost estimation, recommendations, and segmentation — while working closely with our solution architect, the client's CTO, and the client's engineering team to shape how those models fit into the platform's architecture.

Project Length: 3 - 6 months.

Key Responsibilities

• Bayesian modeling and inference: Design and implement Bayesian statistical models — priors, likelihoods, and posterior inference — to support decisioning under uncertainty across pricing, segmentation, and demand-related use cases.
• Markov chains and Hidden Markov Models: Build Markov chain and Hidden Markov Model formulations for sequential and behavioral patterns (e.g., customer lifecycle stages, state transitions), producing outputs that downstream services can consume.
• MCMC and Metropolis-Hastings sampling: Apply Markov Chain Monte Carlo methods, including Metropolis-Hastings sampling, to estimate posterior distributions for models without closed-form solutions, and validate convergence and sampling quality.
• Mixture modeling: Develop mixture models — Gaussian Mixture Models in particular — to support segmentation use cases, identifying latent customer or product groupings from transactional and behavioral data.
• Expectation-Maximization: Implement Expectation-Maximization for latent-variable estimation underlying mixture models and related unsupervised learning tasks.
• Architecture collaboration: Work alongside our solution architect and the client's CTO to align model design with platform architecture. While this is not an architecture-ownership role, you should be able to reason about integration points, service boundaries, and technical tradeoffs well enough to operate with a reasonable degree of autonomy and reduce the support load on the architect.
• Production translation: Guide backend engineering on how statistical models translate into production service architecture — informing API design, data contracts, and integration points within the platform's existing microservices and event-driven pipelines.
• Model lifecycle management: Define the approach for model training, validation, versioning, monitoring/drift detection, and retraining cadence once models are in production.
• Roadmap collaboration: Partner with delivery and engineering leads to size, sequence, and estimate probabilistic/statistical modeling initiatives on the product roadmap.
• Documentation and handoff: Document modeling assumptions, methodology, and validation results, and provide clear hand-off guidance so models remain maintainable by the engineering team after the engagement.

Department: Data Science & Engineering
Function: Information Technology
Experience Level: Mid-Senior Level