Job opportunity
Data Scientist (Masters)
Alignerr
Data Scientist (Masters) — AI Model Trainer
About The Role
What if your expertise in machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason through complex problems? We're looking for data scientists with advanced degrees to challenge, audit, and refine cutting‑edge AI models — helping them think more rigorously, reason more accurately, and perform at a higher level.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Design Advanced Challenges: Develop complex, domain‑specific data science problems spanning hyperparameter optimization, Bayesian inference, cross‑validation strategies, dimensionality reduction, and more — problems that genuinely push AI reasoning to its limits
Author Ground‑Truth Solutions: Write rigorous, step‑by‑step technical solutions including Python and R scripts, SQL queries, and mathematical derivations that serve as authoritative reference answers
Audit AI‑Generated Code: Evaluate AI outputs using libraries like Scikit‑Learn, PyTorch, and TensorFlow — assessing them for technical accuracy, efficiency, and correctness
Identify and Document Failure Modes: Catch logical errors in AI reasoning such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback that improves model behaviour
Refine Model Reasoning: Help shape how AI models approach statistical and algorithmic problems by documenting every meaningful failure and guiding iterative improvements
Who You Are
Currently pursuing or have completed a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong data analysis focus
Solid foundational knowledge across core areas such as supervised and unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
Able to communicate highly technical concepts — algorithmic logic, statistical results, mathematical derivations — clearly and precisely in written form
Naturally detail‑oriented: you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss
Self‑directed and comfortable working independently on technical tasks
No prior AI or data annotation experience required
Nice to Have
Prior experience with data annotation, data quality assurance, or model evaluation systems
Proficiency in production‑level data science workflows such as MLOps or CI/CD pipelines for machine learning models
Familiarity with prompt engineering or working with large language models
Experience writing technical documentation or academic‑style explanations of complex methods
Why Join Us
Work directly with industry‑leading AI research teams and language models at the frontier of the field
Fully remote and flexible — work when and where it suits you, on your own schedule
Freelance autonomy with the structure of meaningful, technically rigorous work
Make a direct, measurable impact on how the next generation of AI understands and applies data science
Potential for ongoing work and contract extension as new projects launch
#J-18808-Ljbffr
Apply on source site About The Role
What if your expertise in machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason through complex problems? We're looking for data scientists with advanced degrees to challenge, audit, and refine cutting‑edge AI models — helping them think more rigorously, reason more accurately, and perform at a higher level.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Design Advanced Challenges: Develop complex, domain‑specific data science problems spanning hyperparameter optimization, Bayesian inference, cross‑validation strategies, dimensionality reduction, and more — problems that genuinely push AI reasoning to its limits
Author Ground‑Truth Solutions: Write rigorous, step‑by‑step technical solutions including Python and R scripts, SQL queries, and mathematical derivations that serve as authoritative reference answers
Audit AI‑Generated Code: Evaluate AI outputs using libraries like Scikit‑Learn, PyTorch, and TensorFlow — assessing them for technical accuracy, efficiency, and correctness
Identify and Document Failure Modes: Catch logical errors in AI reasoning such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback that improves model behaviour
Refine Model Reasoning: Help shape how AI models approach statistical and algorithmic problems by documenting every meaningful failure and guiding iterative improvements
Who You Are
Currently pursuing or have completed a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong data analysis focus
Solid foundational knowledge across core areas such as supervised and unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
Able to communicate highly technical concepts — algorithmic logic, statistical results, mathematical derivations — clearly and precisely in written form
Naturally detail‑oriented: you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss
Self‑directed and comfortable working independently on technical tasks
No prior AI or data annotation experience required
Nice to Have
Prior experience with data annotation, data quality assurance, or model evaluation systems
Proficiency in production‑level data science workflows such as MLOps or CI/CD pipelines for machine learning models
Familiarity with prompt engineering or working with large language models
Experience writing technical documentation or academic‑style explanations of complex methods
Why Join Us
Work directly with industry‑leading AI research teams and language models at the frontier of the field
Fully remote and flexible — work when and where it suits you, on your own schedule
Freelance autonomy with the structure of meaningful, technically rigorous work
Make a direct, measurable impact on how the next generation of AI understands and applies data science
Potential for ongoing work and contract extension as new projects launch
#J-18808-Ljbffr