Code Data Validation Consultant (Machine Learning & Data Processing)

• Join our team to enable cutting-edge AI/ML innovation by building robust data pipelines and automation tools.
• You'll work closely with human data operators and generative AI teams to process, analyze, and optimize high-quality datasets for training machine learning models.
• Your work will directly impact the efficiency and performance of AI systems, from automating data quality checks to designing infrastructure that scales with evolving model requirements.
• This role is ideal for a problem-solver who thrives in fast-paced environments and enjoys bridging data engineering with machine learning.

Responsibilities:

• Data Pipeline Development:
• Design and implement Python-based automation tools to process, clean, and transform raw data for ML training.
• Build custom scripts to streamline data ingestion and preprocessing workflows.
• Quality Analysis & Reporting:
• Conduct manual and automated quality assessments to identify high/low-impact data for model training.
• Generate reports detailing experimental results, data effectiveness, and recommendations for improvement.
• ML Model Integration:
• Train and evaluate open-source ML models (e.g., Gemma) to assess data impact on model performance.
• Collaborate with AI teams to refine data selection strategies based on model feedback.
• Infrastructure Optimization:
• Develop scalable solutions in Colab/Jupyter Notebooks to automate data validation and filtering.
• Troubleshoot and debug data formatting issues (e.g., code-comment relevance, dataset consistency).

• Preferred: 2-3+ years in data analysis/validation/engineering, ML engineering, or automation-focused roles.
• Bonus: PhD graduates with hands-on ML/data processing projects.

Required (Desired):

• Exposure to Generative AI models (e.g., GPT, Llama) or large-scale datasets.
• Bash/Shell Scripting: Ability to automate repetitive tasks.
• Familiarity with APIs for data ingestion/processing.
• Experience contributing to open-source projects or public GitHub repositories.
• Knowledge of cloud services., * Technical Expertise:
• Python: Medium to Advanced proficiency (scripting, automation, data processing libraries like Pandas/NumPy).
• Hands-on experience writing, executing and reviewing code. (Preferably using Colab/Jupyter Notebooks)
• Data & ML Skills:
• Experience training/fine-tuning ML models and analyzing their performance.
• Familiarity with public data platforms (Hugging Face, GitHub) and data formats (JSON, CSV).
• Analytical Skills.
• Proven ability to assess data quality and build tools to automate quality checks., * Bachelor's degree in Computer Science, Data Science, Engineering, or related STEM field.