Data Scientist (Masters)
Alignerr
Mumbai, Maharashtra, India2h agovia LinkedIn
About the Role
Alignerr is seeking data scientists with graduate-level training to stress-test cutting-edge AI models. This is a fully remote, flexible hourly contract role for independent data scientists to work on AI development.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
Key Responsibilities
- Design Advanced Challenges: Create rigorous, domain-specific data science problems covering hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more.
- Build Ground-Truth Solutions: Author precise, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations.
- Audit AI-Generated Code: Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness.
- Sharpen AI Reasoning: Identify logical failures such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback.
- Work Independently: Complete task-based assignments asynchronously, on your own schedule.
Qualifications
- Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysis.
- Deep foundational knowledge in supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP.
- Ability to communicate complex algorithmic concepts and statistical results clearly and concisely in writing.
- Exceptionally detail-oriented, precise when checking code syntax, mathematical notation, and the validity of statistical conclusions.
- Self-motivated and consistent when working independently.
- No prior AI industry experience required.
Nice to Have
- Prior experience with data annotation, data quality evaluation, or AI output review.
- Proficiency in production-level data science workflows: MLOps, CI/CD for models, or model monitoring.
- Familiarity with academic or industry benchmarking methodologies.
- Broad exposure across multiple data science subfields.