Data Scientist – AI, ML

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

📋 Description

• Design and develop Bayesian statistical models for making decisions under uncertainty in areas such as pricing, segmentation, and demand-related applications.

• Create formulations using Markov chains and Hidden Markov Models to analyze sequential and behavioral patterns.

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

• Construct mixture models, specifically Gaussian Mixture Models, for customer or product segmentation purposes.

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

• Convert statistical models into a production service architecture, collaborating with backend engineering to define APIs, data contracts, and integration points for microservices.

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

• Work alongside delivery and engineering leaders to size, sequence, and estimate modeling projects.

• Document modeling assumptions, methodologies, and validation outcomes, and provide guidance for handoff procedures.

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

⛳️ Requirements

• Proficiency in English, both written and spoken, at a minimum of 90% (B2 level or higher).

• Over 5 years of professional experience in data science, focusing on statistical modeling or applied machine learning.

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

• Proficient in Python (or R) and familiar with standard probabilistic/statistical libraries (e.g., PyMC, Stan, scikit-learn, NumPy/SciPy) for model development and validation.

• Capability to translate statistical/mathematical models into a service-oriented production architecture, including defining APIs and data contracts, and collaborating directly with backend engineers for integration.

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

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

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

• Preferred: Experience in integrating ML models with microservices architectures (REST/GraphQL) and event-driven systems (e.g., message queues/pub-sub), as well as deploying to cloud infrastructure.

• Preferred: Familiarity with common backend service ecosystems (e.g., .NET, Java, or Node.js).

• 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 chill atmosphere