Data Scientist – AI, ML

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

📋 Description

• Create and execute Bayesian statistical models, encompassing priors, likelihoods, and posterior inference, tailored for pricing, segmentation, and demand-related applications.

• Develop Markov chain and Hidden Markov Model constructs to analyze sequential and behavioral trends.

• Utilize MCMC techniques, such as Metropolis-Hastings sampling, and ensure the verification of convergence and sampling quality.

• Construct mixture models, with a focus on Gaussian Mixture Models, for the purpose of customer or product segmentation.

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

• Convert statistical models into a production service framework with backend engineering by specifying APIs, data contracts, and integration points.

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

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

• Document the assumptions, methodology, and validation results pertaining to the models.

• Offer guidance for handoff to ensure maintainable ownership by the engineering team following the engagement.

• Assist in supporting a client's e-commerce platform that operates on a distributed microservices architecture.

⛳️ Requirements

• Proficiency in English (written and oral) at a level of +90% (minimum B2).

• Extensive 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 in constructing and deploying statistical/ML models into production environments, beyond mere research notebooks or offline analyses.

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

• Capability to convert statistical/mathematical models into service-oriented production architecture, defining APIs and data contracts while collaborating with backend engineers.

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

• Excellent written and verbal communication skills, capable of conveying model behavior, assumptions, and uncertainty to non-technical stakeholders.

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

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

• Preferred: familiarity with backend ecosystems such as .NET, Java, or Node.js.

• Preferred: experience with MLOps tools including model registries, monitoring, and feature stores.

• Preferred: background in pricing science, recommendation systems, or marketing analytics.

🏝️ Benefits

• Flexible working hours.

• Opportunity to work from any location.

• Referral program available.

• A supportive and relaxed work environment.