Data Scientist (Masters)
Alignerr
Delhi, Delhi, IndiaFresher3h agovia LinkedIn
This job at a glance
- Who can apply
- Freshers
- Must know
- PythonSQLPyTorchTensorFlowscikit-learnAuditing
- Nice to have
- CI/CD
- Work
- Remote
- Pay
- Not stated by the employer.
- Skill demand
- SQL39%Python18%of 28 similar fresher jobs posted in India this week
- Listed
- 30 Oct 2026 · still open
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About the Role
We are seeking expert-level data scientists to challenge, evaluate, and refine cutting-edge AI models. This is a fully remote, flexible contract role. No prior AI industry experience is required.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
Key Responsibilities
- Design Advanced Challenges: Craft complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more.
- Author Ground-Truth Solutions: Develop rigorous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations.
- Audit AI-Generated Code: Critically evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow, assessing technical accuracy, efficiency, and correctness.
- Expose Reasoning Failures: Identify and document logical flaws in AI reasoning (e.g., data leakage, overfitting, improper handling of imbalanced datasets) and provide structured feedback.
Qualifications
- Currently 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.
- Deeply knowledgeable in core data science domains: supervised/unsupervised learning, deep learning, statistical inference, big data technologies (Spark, Hadoop), or NLP.
- A precise and analytical writer, capable of explaining complex algorithmic concepts and statistical results clearly in written form.
- Naturally detail-oriented, with an ability to catch errors in code syntax, mathematical notation, and statistical logic.
- No prior AI or data annotation experience required.
Nice to Have
- Prior experience with data annotation, data quality, or AI evaluation workflows.
- Proficiency in production-level data science practices: MLOps, CI/CD pipelines for models, or model monitoring.
- Familiarity with evaluation rubrics or quality assurance processes in technical contexts.