Data Scientist

• Own model and agent behavior end to end, from problem framing and algorithm selection through fine-tuning, retrieval design, agentic orchestration, evaluation, and production serving
• Design experiments, interpret model implementations, and ship production solutions within latency budgets
• Determine what agent memory should retain, in what form, and how retention is grounded in a governed data warehouse rather than an undifferentiated vector blob
• Partner closely with the AI Data Engineer, who owns the warehouse, pipelines, and index infrastructure
• Own the algorithm, prompt, and evaluation while collaborating on pipeline, schema, and data guarantees

Senior AI-ML Data Scientist

• Design and validate probabilistic models for an e-commerce platform
• Develop models for dynamic pricing, shipping cost estimation, recommendations, and customer/product segmentation
• Design and implement Bayesian statistical models, including priors, likelihoods, and posterior inference
• Build Markov chain and Hidden Markov Model formulations for sequential and behavioral patterns
• Apply MCMC methods, including Metropolis-Hastings sampling, and validate convergence and sampling quality
• Develop mixture models, particularly Gaussian Mixture Models, for segmentation
• Implement Expectation-Maximization for latent-variable estimation and unsupervised learning
• Collaborate with the solution architect and client's CTO on platform architecture alignment
• Guide backend engineering on production translation, API design, data contracts, and microservice/event-driven integration
• Define model training, validation, versioning, monitoring, drift detection, and retraining approaches
• Partner with delivery and engineering leads to size, sequence, and estimate modeling initiatives
• Document modeling assumptions, methodology, validation results, and handoff guidance

Senior Data Scientist, Statistical Modeling

• Design transactional and credit behavior features, including frequency, recency, amount, digital engagement, and delinquency, for the risk model
• Implement features with complete standard metadata, including description, PKs/FKs, lineage, owner, tags, data lifecycle, temporality, and D1/D2 design to maximize reuse
• Follow the deployment pipeline from bundle validation through DDLs and PRD
• Monitor jobs and follow FinOps practices
• Ensure consistency between training and production as the model's single source of truth
• Work closely with modeling and platform teams
• Turn transactional and credit behavior data into trustworthy, well-governed features

Data Scientist jobs in Argentina

Senior AI-ML Data Scientist

Senior Data Scientist, Statistical Modeling

Data Scientist – Feature Engineering, Databricks