Prompt & Python Data Analytics Developer

Role Overview:

This role blends prompt engineering, Python development, and data analytics, ensuring outputs are validated with robust datasets and statistical methods. The ideal candidate is skilled in Python standard libraries and comfortable with pandas, NumPy, and related tools for data-driven problem solving. 

Key Responsibilities-

Prompt Engineering & Integration:

Python Development:

Data Analytics & Processing:

Testing & Validation:

Collaboration:

Required Qualifications:

• Proficiency in Python (3.x) with strong knowledge of standard libraries.
• Hands-on experience with pandas and NumPy for data analytics.
• Familiarity with data wrangling, preprocessing, and transformation techniques.
• Experience in prompt engineering and integrating prompts into applications.
• Strong understanding of testing frameworks (pytest, unittest).
• Version control experience (Git/GitHub).
• Familiarity with project management tools (Jira).

Preferred Qualifications:

• Familiarity with visualization libraries (e.g., matplotlib, seaborn).
• Experience handling large datasets and performance optimization.
• Knowledge of NLP evaluation techniques.
• Exposure to SQL or other data querying tools.

Key Competencies:

• Analytical Mindset Skilled at uncovering insights from data.
• Technical Rigor Builds clean, maintainable Python + analytics pipelines.
• Problem-Solving – Uses data to validate, optimize, and refine workflows.
• Collaboration – Works effectively across data, engineering, and product teams.
• Attention to Detail – Ensures data quality, reproducibility, and transparency.

Success Metrics:

• High-quality datasets and analytics pipelines supporting model development.
• Accurate and reproducible analyses of model outputs and workflows.
• Efficient Python code that integrates analytics with prompt-based systems.
• Strong collaboration and clear communication of data-driven findings.