Data Scientist – AI, ML

This is a fully remote position, open to applicants in Mali.

📋 Description

• Design and execute Bayesian statistical models for decision-making under uncertainty in areas such as pricing, segmentation, and demand-related scenarios.

• Construct Markov chain and Hidden Markov Model frameworks to analyze sequential and behavioral patterns.

• Utilize MCMC techniques, including Metropolis-Hastings sampling, to estimate posterior distributions while ensuring convergence and sampling quality are validated.

• Create mixture models, specifically Gaussian Mixture Models, to facilitate customer or product segmentation.

• Implement Expectation-Maximization for estimating latent variables and performing unsupervised learning tasks.

• Convert statistical models into a production service architecture by collaborating with backend engineering teams to define APIs, data contracts, and integration points within microservices and event-driven pipelines.

• Outline model training, validation, versioning, monitoring, drift detection, and retraining strategies.

• Work alongside delivery and engineering leads to size, prioritize, and estimate modeling projects.

• Document modeling assumptions, methodologies, and validation outcomes.

• Provide guidance for hand-offs to ensure models remain maintainable by the engineering team after the engagement concludes.

⛳️ Requirements

• Proficiency in English, both written and spoken (minimum B2 level), with outstanding communication abilities.

• Strong and demonstrable expertise in Bayesian statistics/Bayesian inference, Markov chains, Hidden Markov Models, MCMC methods (including Metropolis-Hastings sampling), mixture models (preferably Gaussian Mixture Models), and Expectation-Maximization.

• Proven experience in developing and deploying statistical/ML models in production environments, rather than solely in research notebooks or offline analyses.

• Proficient in Python (or R) with experience in standard probabilistic/statistical libraries such as PyMC, Stan, scikit-learn, and NumPy/SciPy.

• Ability to transform statistical/mathematical models into service-oriented production architectures, defining APIs and data contracts while collaborating directly with backend engineers.

• Strong understanding of version control systems, testing methodologies, and CI/CD practices.

• Excellent written and verbal communication skills, with the capability to articulate model behavior, assumptions, and uncertainties to non-technical stakeholders.

• Experience in e-commerce or retail sectors, particularly in pricing optimization, customer segmentation, or demand forecasting (preferred).

• Familiarity with integrating ML models within microservices architectures (REST/GraphQL), event-driven systems, and cloud infrastructure (preferred).

• Knowledge of .NET, Java, or Node.js backend ecosystems (preferred).

• Experience with MLOps tools such as model registries, monitoring systems, and feature stores (preferred).

• Background in pricing science, recommendation systems, or marketing analytics (preferred).

• Capability to work from a LATAM country.

🏝️ Benefits

• Access to educational resources.

• Flexible schedule and the ability to work from anywhere.

• Referral program.

• Supportive and relaxed working environment.

• Recognition plan for career trajectory.