Data Scientist - Gen AI / QA

This position is posted by Jobgether on behalf of Zyte. We are currently looking for a Data Scientist - Gen AI / QA in Argentina, Brazil, Uruguay, Poland, Spain, Portugal, UK, Ireland, Croatia, Hungary, Slovenia. As a Data Scientist specializing in Generative AI and QA, you’ll play a key role in advancing data quality validation processes for large-scale web-extracted data. This is a highly technical, detail-oriented position where you will apply Python, AI, and data visualization techniques to detect and resolve data inconsistencies, automate quality checks, and deliver trusted results. You’ll collaborate with cross-functional teams in a fully remote, global environment, supporting enterprise-grade customers. If you're passionate about leveraging AI to solve data problems at scale, this is a high-impact opportunity in a fast-evolving space. Accountabilities Design and implement AI-powered data quality validation techniques based on customer-specific web scraping requirements. Analyze and summarize large datasets through descriptive statistics, summaries, and visualizations to uncover quality issues. Extend manual QA processes with automated, AI-driven verification tools. Communicate findings and insights clearly to both technical and non-technical stakeholders, including QA engineers, developers, PMs, and clients. Collaborate with the engineering team to identify root causes of data issues and propose technical solutions. Write robust, reusable code following best practices in Python and version control systems such as GitHub or BitBucket. Requirements At least 3 years of hands-on experience with Python and the PyData stack (Pandas, NumPy, etc.). Solid understanding of Generative AI, especially in the context of web scraping, parsing, and data quality automation. Comfortable with prompt engineering and optimization techniques (token/cost control). Familiarity with frameworks like MCP, Marvin, Langchain, and experience with OpenAI, Google, or Anthropic APIs. Skilled in analyzing and visualizing large-scale datasets (millions of records), with strong attention to detail. Working knowledge of QA methodologies and tools for validating structured and unstructured data. BS in Computer Science, Engineering, Mathematics, Statistics, or a related field. Excellent communication skills in English, with the ability to explain technical concepts to varied audiences. Preferred: Prior experience in a Data QA role focused on data integrity rather than application testing. Familiarity with Jupyter/JupyterLab, Spark, BigQuery, and dashboard creation. Experience in fully remote, globally distributed teams. Benefits Join a diverse, self-motivated team across 28+ countries. Enjoy full flexibility to work remotely from where you perform best. Collaborate on cutting-edge data technologies and open-source tools. Attend global conferences and regular team events. Thrive in a culture that values ownership, innovation, and continuous learning. Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching. When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly. Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements. It compares your profile to the job’s core requirements and past success factors to determine your match score. Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role. When necessary, our human team may perform an additional manual review to ensure no strong profile is missed. The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team. Thank you for your interest! Originally posted on Himalayas