Senior Data Scientist, Statistical Modeling

Software Mind

. Design and validate probabilistic models for an e-commerce platform .

Posted 9/25/2026full-timeRemote • Romania, ArgentinaSeniorWebsite

Tech Stack

Tools & technologies

GraphQLJavaJavaScriptMicroservicesNode.jsNumpyPythonScikit-Learn.NET

About the role

Key responsibilities & impact

Design and validate probabilistic models for an e-commerce platform
Develop models for dynamic pricing, shipping cost estimation, recommendations, and customer/product segmentation
Design and implement Bayesian statistical models, including priors, likelihoods, and posterior inference
Build Markov chain and Hidden Markov Model formulations for sequential and behavioral patterns
Apply MCMC methods, including Metropolis-Hastings sampling, and validate convergence and sampling quality
Develop mixture models, particularly Gaussian Mixture Models, for segmentation
Implement Expectation-Maximization for latent-variable estimation and unsupervised learning
Collaborate with the solution architect and client's CTO on platform architecture alignment
Guide backend engineering on production translation, API design, data contracts, and microservice/event-driven integration
Define model training, validation, versioning, monitoring, drift detection, and retraining approaches
Partner with delivery and engineering leads to size, sequence, and estimate modeling initiatives
Document modeling assumptions, methodology, validation results, and handoff guidance

Requirements

What you’ll need

90% English written and oral proficiency, at least B2 level
Senior-level experience communicating confidently with technical and business stakeholders, including CTO-level discussions
Demonstrable expertise designing Bayesian statistical models, Markov chains, Hidden Markov Models, MCMC methods including Metropolis-Hastings, mixture models including Gaussian Mixture Models, and Expectation-Maximization
Experience with classical predictive modeling rather than standard modern supervised/LLM-based ML
Experience designing statistical/ML models with production deployment in mind preferred; hands-on production implementation is a plus but not mandatory
Proficiency in Python or R
Experience with probabilistic/statistical libraries such as PyMC, Stan, scikit-learn, NumPy/SciPy
Ability to translate statistical/mathematical models into service-oriented production architecture
Understanding of APIs, data contracts, backend engineering collaboration, and architecture integration
Solid understanding of version control, testing practices, and CI/CD
Strong written and verbal communication skills
Experience in e-commerce or retail domains preferred
Familiarity with REST/GraphQL, microservices, and event-driven systems preferred
Familiarity with .NET, Java, or Node.js backend ecosystems preferred
Exposure to MLOps concepts such as model registries, monitoring, or feature stores preferred
Background in pricing science, recommendation systems, or marketing analytics preferred
Experience communicating modeling recommendations to business or executive stakeholders preferred

Benefits

Comp & perks

Excellent work environment certified by Great Place To Work
Remote work option
Project engagement of 3–6 months