Data Scientist, AI, ML

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

📋 Description

• Create and implement Bayesian statistical models for decision-making in uncertain environments, focusing on pricing, segmentation, and demand-related scenarios.

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

• Utilize MCMC techniques, including Metropolis-Hastings sampling, to estimate posterior distributions and assess convergence and sampling quality.

• Develop mixture models, especially Gaussian Mixture Models, for the purpose of customer or product segmentation.

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

• Convert statistical models into operational service architecture, incorporating APIs, data contracts, and integration points within microservices and event-driven pipelines.

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

• Collaborate with delivery and engineering leads to scope, sequence, and estimate roadmap initiatives.

• Document assumptions, methodologies, and validation outcomes related to modeling.

• Provide guidance for the engineering team to ensure maintenance post-engagement.

• Work in partnership with backend engineering to facilitate production delivery.

⛳️ Requirements

• Proficient in English, both written and verbal, with a minimum of 90% fluency (at least B2 level).

• Exceptional communication abilities.

• Strong, 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 track record of building and deploying statistical/ML models within production environments.

• Proficiency in Python or R, particularly with probabilistic/statistical libraries such as PyMC, Stan, scikit-learn, and NumPy/SciPy.

• Capability to translate statistical/mathematical models into service-oriented production architecture, including the definition of APIs and data contracts.

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

• Strong written and spoken communication skills.

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

• Preferred: experience with integrating ML models into microservices architectures, REST/GraphQL, event-driven systems, and cloud infrastructure.

• Preferred: familiarity with .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

• Flexible schedule.

• Work from anywhere.

• Referral program.

• Supportive and relaxed work environment.