17 hours ago

Data Science Expert - AI Content Specialist

Alignerr

Hybrid
Contractor
$140,000
Hybrid

Job Overview

Job TitleData Science Expert - AI Content Specialist
Job TypeContractor
Offered Salary$140,000
LocationHybrid

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Job Description

Data Science Expert - AI Content Specialist at Alignerr

At Alignerr, we partner with the world’s leading AI research teams and labs to build and train cutting-edge AI models. You’ll challenge advanced language models on topics like machine learning theory, statistical inference, neural network architectures, and data engineering pipelines—documenting every failure mode so we can harden model reasoning.

Organization: Alignerr

Position: Data Science Expert - AI Content Specialist

Type: Hourly Contract

Compensation: $40–$80 /hour

Location: Remote

Commitment: 10–40 hours/week

What You'll Do

  • Develop Complex Problems: Design advanced data science challenges across domains like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction.
  • Author Ground-Truth Solutions: Create rigorous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as "golden responses."
  • Technical Auditing: Evaluate AI-generated code (using libraries like Scikit-Learn, PyTorch, or TensorFlow), data visualizations, and statistical summaries for technical accuracy and efficiency.
  • Refine Reasoning: Identify logical fallacies in AI reasoning—such as data leakage, overfitting, or improper handling of imbalanced datasets—and provide structured feedback to improve the model's "thinking" process.

Requirements

  • Advanced Degree: Masters (pursuing or completed) or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a heavy emphasis on data analysis.
  • Domain Expertise: Strong foundational knowledge in core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP.
  • Analytical Writing: The ability to communicate highly technical algorithmic concepts and statistical results clearly and concisely in written form.
  • Attention to Detail: High level of precision when checking code syntax, mathematical notation, and the validity of statistical conclusions.

No AI experience required

Preferred Qualifications

  • Prior experience with data annotation, data quality, or evaluation systems.
  • Proficiency in production-level data science workflows (e.g., MLOps, CI/CD for models).

Why Join Alignerr

  • Excellent compensation with location-independent flexibility.
  • Direct engagement with industry-leading LLMs.
  • Contractor advantages: high agency, agility, and international reach.
  • More opportunities for contracting renewals.

Application Process

The application process takes approximately 15-20 minutes and involves:

  • Submitting your resume.
  • Completing a short screening.
  • Project matching and onboarding.

Our team reviews applications daily. Please ensure you complete your AI interview and application steps for consideration.

Key Skills/Competencies

  • Data Science
  • Machine Learning
  • Statistical Inference
  • Python/R
  • SQL
  • Neural Networks
  • Deep Learning
  • Natural Language Processing (NLP)
  • Technical Writing
  • AI Model Evaluation

Tags:

Data Science Expert
AI Content Specialist
machine learning
statistical inference
data analysis
Python
R
SQL
deep learning
NLP
AI model evaluation
hyperparameter optimization
Bayesian inference
Scikit-Learn
PyTorch
TensorFlow
Spark
Hadoop
MLOps
CI/CD

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How to Get Hired at Alignerr

  • Research Alignerr's mission: Study their work with leading AI research teams and focus on advanced model training.
  • Tailor your resume: Highlight advanced degrees in quantitative fields, strong data science expertise, and analytical writing skills.
  • Showcase problem-solving skills: Prepare examples of designing complex data science challenges and authoring rigorous technical solutions.
  • Demonstrate analytical writing: Be ready to present clear, concise explanations of technical and algorithmic concepts.
  • Emphasize attention to detail: Provide instances where your precision in code syntax, mathematical notation, and statistical validity was crucial.

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