Data Scientist, AI, ML

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

📋 Description

• Design and execute Bayesian statistical models aimed at facilitating decision-making under uncertainty in areas such as pricing, segmentation, and demand-related applications.

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

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

• Create mixture models, particularly Gaussian Mixture Models, for the purposes of customer or product segmentation.

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

• Convert statistical models into production service frameworks by collaborating with backend engineering to define 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 leaders to size, sequence, and estimate modeling projects.

• Document assumptions, methodologies, and validation results related to the models.

• Offer guidance during handoff to ensure models remain maintainable post-engagement.

⛳️ Requirements

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

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

• Proven track record of developing and deploying statistical/ML models within production systems, rather than solely in research notebooks or offline analyses.

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

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

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

• Excellent written and verbal communication abilities, with the capacity to clarify model behavior, assumptions, and uncertainty to non-technical audiences.

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

• Preferred: Experience connecting ML models with microservices architectures (REST/GraphQL) and event-driven systems (message queues/pub-sub), along with deployment to cloud infrastructures.

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

• Preferred: Experience utilizing MLOps tools, including model registries, monitoring, 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