Bayesian ML Data Scientist - Remote & Flexible

Software Mind Americas; Buenos Aires; Híbrido; ARS 1.800.000 - 3.200.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible schedule and Work From Anywhe

Referral Program

Supportive and chill atmosphere

Descripción de la vacante

Software Mind Americas is seeking a Data Scientist with deep expertise in probabilistic AI and statistical machine learning to support a client’s e-commerce platform, built on a distributed microservices architecture. The role focuses on designing, validating, and productionizing probabilistic models within the platform’s existing microservices ecosystem.

Responsibilities include Bayesian modeling, Bayesian inference, and implementing mixtures, EM, and MCMC methods, with close collaboration to

Formación

• Strong background in Bayesian statistics and inference.
• Experience with Markov chains, Hidden Markov Models, and MCMC methods.
• Proven ability to productionize statistical/ML models into services.
• Proficiency in Python with probabilistic libraries (PyMC, Stan).

Responsabilidades

• Design Bayesian models for pricing, segmentation, and demand.
• Develop sequential models like Markov chains and HMMs.
• Apply MCMC (Metropolis-Hastings) to estimate posteriors and validate convergence.
• Build Gaussian Mixture Models for customer/product segmentation.
• Implement EM for latent-variable estimation.
• Collaborate with backend to translate models into production APIs.
• Define model training, validation, and monitoring workflows.
• Document modeling assumptions and validation results.
• Support roadmap sizing and prioritization with engineering leads.

Conocimientos

Bayesian statistics

Markov chains

Hidden Markov Models

MCMC methods

Gaussian Mixture Models

EM algorithm

Python

Herramientas

PyMC

Stan

scikit-learn

NumPy/SciPy

Descripción del empleo

Software Mind Americas is seeking a Data Scientist with deep expertise in probabilistic AI and statistical machine learning to support a client’s e-commerce platform, built on a distributed microservices architecture. The role focuses on designing, validating, and productionizing probabilistic models within the platform’s existing microservices ecosystem.

Responsibilities include Bayesian modeling, Bayesian inference, and implementing mixtures, EM, and MCMC methods, with close collaboration to