Data Scientist – AI, ML

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

📋 Description

• Create and execute Bayesian statistical models to facilitate decision-making in situations of uncertainty across pricing, segmentation, and demand-related scenarios.

• Develop formulations for Markov chains and Hidden Markov Models to analyze sequential and behavioral trends.

• Utilize MCMC techniques, including Metropolis-Hastings sampling, while ensuring the validation of convergence and sampling quality.

• Construct mixture models, especially Gaussian Mixture Models, for the segmentation of customers or products.

• Apply Expectation-Maximization methods for latent-variable estimation and tasks involving unsupervised learning.

• Convert statistical models into production service architecture by engaging in backend engineering, defining APIs, data contracts, and integration points for microservices.

• Establish processes for model training, validation, versioning, monitoring, drift detection, and retraining.

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

• Document assumptions related to modeling, methodologies, and outcomes of validations.

• Offer guidance for handoff to engineering teams to ensure model maintainability.

⛳️ Requirements

• Proficient in English with a minimum of +90% in both written and spoken form (at least B2 level).

• Solid and demonstrable experience 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 track record of building and deploying statistical or machine learning models within production systems rather than solely in research notebooks or offline analyses.

• Strong proficiency in Python (or R), utilizing standard probabilistic/statistical libraries (e.g., PyMC, Stan, scikit-learn, NumPy/SciPy).

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

• Comprehensive understanding of version control, testing methodologies, and CI/CD processes.

• Excellent written and verbal communication abilities, with the skill to convey model behavior, assumptions, and uncertainties to non-technical audiences.

• Preferred: Experience in e-commerce or retail sectors, especially in pricing optimization, customer segmentation, or demand forecasting.

• Preferred: Familiarity with integrating machine learning models into microservices architectures (REST/GraphQL), along with event-driven systems and cloud infrastructures.

• 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.

🏝️ Benefits

• Access to educational resources.

• Flexible schedule with the option to work from anywhere.

• Incentives through a referral program.

• A supportive and relaxed work environment.

• Recognition plan for career trajectory.