Data Scientist, AI, ML

Software Mind

. Design and implement Bayesian statistical models for decisioning under uncertainty across pricing, segmentation, and demand-related use cases .

Posted 9/21/2026full-timeRemote • MaliMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Bayesian statistics, Markov models, and MCMC methods, with a strong ability to translate statistical models into production architectures. Proficient in Python or R for building and deploying statistical models, with a solid understanding of CI/CD practices and microservices integration.

Highest-signal resume keywords

Bayesian StatisticsMarkov ChainsMCMC MethodsPython or R ProficiencyStatistical Model Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Bayesian InferenceHidden Markov ModelsMixture ModelsExpectation-MaximizationAPIs and Data ContractsModel Training and ValidationVersion ControlTesting PracticesCI/CDStatistical Libraries

Soft Skills

Excellent Communication SkillsCollaboration

Tools & Technologies

PyMCStanScikit-learnNumPySciPyMLOps Tooling.NETJavaNode.jsCloud Infrastructure

Industry Keywords

E-commerceRetailPricing OptimizationCustomer SegmentationDemand ForecastingRecommendation SystemsMarketing Analytics

Tech Stack

Tools & technologies

CloudGraphQLJavaJavaScriptMicroservicesNode.jsNumpyPythonScikit-Learn.NET

About the role

Key responsibilities & impact

Design and implement Bayesian statistical models for decisioning under uncertainty across pricing, segmentation, and demand-related use cases
Build Markov chain and Hidden Markov Model formulations for sequential and behavioral patterns
Apply MCMC methods, including Metropolis-Hastings sampling, to estimate posterior distributions and validate convergence and sampling quality
Develop mixture models, particularly Gaussian Mixture Models, for customer or product segmentation
Implement Expectation-Maximization for latent-variable estimation and unsupervised learning tasks
Translate statistical models into production service architecture with APIs, data contracts, and integration points in microservices and event-driven pipelines
Define model training, validation, versioning, monitoring, drift detection, and retraining approaches
Partner with delivery and engineering leads to size, sequence, and estimate roadmap initiatives
Document modeling assumptions, methodology, and validation results
Provide handoff guidance for engineering team maintenance after the engagement
Collaborate with backend engineering on production delivery

Requirements

What you’ll need

+90% English written and oral (at least B2 level)
Excellent communication skills
Strong, demonstrable background in Bayesian statistics/Bayesian inference, Markov chains, Hidden Markov Models, MCMC methods including Metropolis-Hastings sampling, mixture models, ideally Gaussian Mixture Models, and Expectation-Maximization
Proven experience building and deploying statistical/ML models into production systems
Proficiency in Python or R with probabilistic/statistical libraries such as PyMC, Stan, scikit-learn, NumPy/SciPy
Ability to translate statistical/mathematical models into service-oriented production architecture, defining APIs and data contracts
Solid understanding of version control, testing practices, and CI/CD
Strong written and verbal communication skills
Preferred: experience in e-commerce or retail, pricing optimization, customer segmentation, or demand forecasting
Preferred: experience integrating ML models with microservices architectures, REST/GraphQL, event-driven systems, and cloud infrastructure
Preferred: familiarity with .NET, Java, or Node.js backend ecosystems
Preferred: experience with MLOps tooling such as model registries, monitoring, and feature stores
Preferred: background in pricing science, recommendation systems, or marketing analytics

Benefits

Comp & perks

Flexible schedule
Work From Anywhere
Referral Program
Supportive and chill atmosphere