INTL - LATAM - Data Scientist

Insight Global • San Francisco, CA • 2h ago

Job Description

Required Skills:

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

4+ years Key (or equivalent) in data science/applied ML, with hands-on LLM experience in real products.

Demonstrated experience deploying and operating ML/LLM systems in production (not just research/prototyping), including monitoring and iteration.

Strong programming skills in Python and comfort with production-grade software practices (testing, code review, versioning, CI/CD collaboration).

Solid understanding of modern NLP/LLM techniques: prompting, RAG, fine-tuning/adaptation, embeddings, reranking, and evaluation.

Experience designing experiments and using statistical methods to measure business impact.

Comfortable working in a fast-paced, agile eCommerce environment

Ability to communicate clearly with technical and non-technical stakeholders; strong product intuition. Preferred Qualifications:

Experience in eCommerce/retail domains (catalog, search, recommendations, personalization, merchandising, content ops).

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