Data Scientist – AI, ML
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Job Description
Data Scientist designing Bayesian and statistical ML models for Software Mind’s e-commerce platform clients. Productionizing models within microservices and event-driven architectures.
Responsibilities:
- Design and implement Bayesian statistical models for decisioning under uncertainty across pricing, segmentation, and demand-related use cases
- Build Markov chain and Hidden Markov Model formulations for sequential and behavioral patterns
- Apply MCMC methods, including Metropolis-Hastings sampling, to estimate posterior distributions and validate convergence and sampling quality
- Develop mixture models, particularly Gaussian Mixture Models, for customer or product segmentation
- Implement Expectation-Maximization for latent-variable estimation and unsupervised learning tasks
- Translate statistical models into production service architecture with backend engineering, defining APIs, data contracts, and integration points within microservices and event-driven pipelines
- Define model training, validation, versioning, monitoring, drift detection, and retraining approaches
- Collaborate with delivery and engineering leads to size, sequence, and estimate modeling initiatives
- Document modeling assumptions, methodology, and validation results
- Provide hand-off guidance so models remain maintainable by the engineering team after the engagement
Requirements:
- +90% English written and oral (at least B2 level) with excellent communication skills
- Strong, demonstrable background in Bayesian statistics/Bayesian inference, Markov chains, Hidden Markov Models, MCMC methods (including Metropolis-Hastings sampling), mixture models (ideally Gaussian Mixture Models), and Expectation-Maximization
- Proven experience building and deploying statistical/ML models into production systems, not just research notebooks or offline analysis
- Proficiency in Python (or R) with standard probabilistic/statistical libraries such as PyMC, Stan, scikit-learn, NumPy/SciPy
- Ability to translate statistical/mathematical models into service-oriented production architecture, defining APIs and data contracts and working directly with backend engineers
- Solid understanding of version control, testing practices, and CI/CD
- Strong written and verbal communication skills, with ability to explain model behavior, assumptions, and uncertainty to non-technical stakeholders
- Experience in e-commerce or retail domains, particularly pricing optimization, customer segmentation, or demand forecasting (preferred)
- Experience integrating ML models with microservices architectures (REST/GraphQL), event-driven systems, and cloud infrastructure (preferred)
- Familiarity with .NET, Java, or Node.js backend ecosystems (preferred)
- Experience with MLOps tooling such as model registries, monitoring, and feature stores (preferred)
- Background in pricing science, recommendation systems, or marketing analytics (preferred)
- Ability to work from a LATAM country
Benefits:
- Educational resources
- Flexible schedule and Work From Anywhere
- Referral Program
- Supportive and chill atmosphere
- Trajectory recognition plan
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