Prompt & Python Data Analytics Developer

As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them.

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Job Reference: #LI-JC1

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:

Create, refine, and optimize prompts for language model applications.

Automate prompt workflows in Python for scalable production use.

Python Development:

Write modular, maintainable Python code leveraging standard libraries.

Data Analytics & Processing:

Use pandas and NumPy to clean, transform, and analyze datasets.

Conduct exploratory data analysis (EDA) to uncover trends and support model improvements.

Implement reproducible data workflows and maintain dataset documentation.

Testing & Validation:

Build test suites for both code and prompt-based workflows.

Apply data-driven validation techniques to measure accuracy and reliability.

Collaboration:

Partner with data scientists and ML engineers to support analytics and evaluation tasks.

Share findings, document workflows, and contribute to best practices.

• 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).

• 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.

• 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.

• 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.