[8BE] Senior Data Scientist, Probabilistic Modeling (Architecture, Adjacent)

Software Mind; Argentina; On-site; ARS 1,800,000 - 3,000,000; Full time

Job summary

Software Mind seeks a Senior Data Scientist with expertise in probabilistic AI to support an e-commerce client in Argentina. You will design and validate Bayesian models for pricing, shipping cost estimation, recommendations, and segmentation, and work closely with the solution architect, CTO, and engineering team to integrate models into the platform.

The engagement lasts 3–6 months, with a focus on production-ready design, model lifecycle, and clear communication of results to technical and

Qualifications

• Deep expertise in Bayesian statistics and probabilistic ML.
• Experience with production deployment and API integration.
• Excellent communication skills in English.
• Ability to work with cross-functional teams.

Responsibilities

• Design Bayesian models for pricing, segmentation, and demand.
• Develop Markov chains and HMMs for sequential data.
• Apply MCMC sampling and validate convergence.
• Collaborate with solution architect and CTO on architecture.
• Document modeling methods and validation results.
• Translate models into production-ready services.

Skills

Bayesian modeling

Probabilistic ML

MCMC methods

Python

Statistical modeling

Tools

PyMC

Stan

scikit-learn

NumPy/SciPy

Job description

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.
• 90% English written and oral (at least B2 level) with excellent communication skills.
• Senior-level experience, with the ability to communicate confidently with both technical and business stakeholders, should be comfortable discussing business impact and tradeoffs directly with CTO.
• Strong, demonstrable background in designing Bayesian statistical models, Markov chains, Hidden Markov Models, MCMC methods (including Metropolis-Hastings sampling), mixture models (ideally Gaussian Mixture Models), and Expectation-Maximization — classical predictive modeling, not standard modern supervised/LLM-based ML.
• Experience designing statistical/ML models with production deployment in mind is strongly preferred; hands-on production implementation is a plus but not mandatory — the priority is the ability to architect the modeling approach and guide engineering through it.
• Proficiency in Python (or R) with standard probabilistic/statistical libraries (e.g., PyMC, Stan, scikit-learn, NumPy/SciPy) for model development and validation.
• Ability to reason about how statistical/mathematical models translate into service-oriented production architecture — understanding of APIs, data contracts, and how to work directly with backend engineers and architects to integrate models.
• Solid understanding of version control, testing practices, and CI/CD, sufficient to collaborate effectively with an engineering team on production delivery.
• Strong written and verbal communication skills, with the ability to explain model behavior, assumptions, and uncertainty to non-technical stakeholders.

Preferred Qualifications/Nice to have

• Experience in e-commerce or retail domains, particularly pricing optimization, customer segmentation, or demand forecasting.
• Familiarity with how ML models integrate into microservices architectures (REST/GraphQL) and event-driven systems (e.g., message queues/pub-sub) hands-on deployment experience is a plus but not expected.
• Familiarity with common backend service ecosystems (e.g., .NET, Java, or Node.js) even if modeling itself is done in Python — for a smoother handoff to the production engineering team.
• Exposure to MLOps concepts such as model registries, monitoring, or feature stores — helpful for handoff conversations, but not a core requirement.
• Background in pricing science, recommendation systems, or marketing analytics.
• Experience communicating modeling recommendations directly to business or executive stakeholders (e.g., CEO/CTO-level conversations).

We are accepting applications from LATAM countries We are Software Mind , an awesome team of engineers who are ready to ramp up any top-notch company’s projects! Our aim? To always be one step ahead. Become part of a multicultural company in constant growth with an excellent work environment certified by Great Place To Work!