3 days ago

Data Scientist (Masters)

Alignerr

Adelaide

3 days ago

Apply

Alignerr is seeking Data Scientists with advanced degrees to work as AI Data Trainers on a fully remote, flexible hourly contract. The role involves designing complex data science challenges, authoring ground-truth solutions, auditing AI-generated code, and identifying reasoning failures to help train and evaluate cutting-edge AI models.

Core AIRemoteContractEntry LevelMachine LearningStatistical Modeling

Data Scientist (Masters)

Apply

Core AIRemoteContractEntry LevelMachine Learning

Salary

Not specified

Work Location

Adelaide, South Australia, Australia, AU

Work Model

Fully remote and asynchronous

Employment Type

Hourly Contract

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)

Machine LearningStatistical ModelingData EngineeringPythonRSQLScikit-LearnPyTorchTensorFlowApache SparkApache HadoopHyperparameter OptimizationBayesian InferenceCross-ValidationDimensionality Reduction

Soft Skills

CommunicationAttention to DetailSelf-DirectedIndependent Work

Preferred Qualifications

Technical (Nice-to-have)

Data AnnotationData Quality EvaluationAI Evaluation WorkflowsMLOpsCI/CDModel Deployment PipelinesNLPComputer VisionApplied Machine Learning

Key Responsibilities

•Design Advanced Challenges — Create complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
•Author Ground-Truth Solutions — Write rigorous, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as the benchmark AI models are trained against
•Audit AI-Generated Code — Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness
•Identify Reasoning Failures — Spot and document logical errors in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback that sharpens model thinking
•Improve Model Reasoning — Document failure modes and edge cases so AI systems can be hardened against real-world data science pitfalls

Data ScientistAI TrainerMachine LearningRemoteContractPythonSQLPyTorchTensorFlowAI Evaluation

Key Responsibilities

•Design Advanced Challenges — Create complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more

•Author Ground-Truth Solutions — Write rigorous, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as the benchmark AI models are trained against

•Audit AI-Generated Code — Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness

•Identify Reasoning Failures — Spot and document logical errors in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback that sharpens model thinking

•Improve Model Reasoning — Document failure modes and edge cases so AI systems can be hardened against real-world data science pitfalls