[8BE] Senior Data Scientist (Statistical Modeling)

Responsibilities

• 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.
• 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.
• Implement Expectation-Maximization for latent-variable estimation underlying mixture models and related unsupervised learning tasks.
• 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.
• 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.
• Define the approach for model training, validation, versioning, monitoring/drift detection, and retraining cadence once models are in production.
• Partner with delivery and engineering leads to size, sequence, and estimate probabilistic/statistical modeling initiatives on the product roadmap.

Requirements

• 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.
• Solid understanding of version control, testing practices, and CI/CD, sufficient to collaborate effectively with an engineering team on production delivery.

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

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.

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.

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.

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.

Strong written and verbal communication skills, with the ability to explain model behavior, assumptions, and uncertainty to non-technical stakeholders.

Preferred

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!