Own outcomes: break projects into milestones, estimate realistically, meet deadlines, and surface risks early with options.
Build and deploy models: develop ML models (including LLM-enabled solutions when appropriate), design features, evaluate rigorously, and contribute to production-grade pipelines.
Work the full data lifecycle: wrangle and clean data, implement data quality checks, write maintainable Python/SQL, add tests, and document decisions.
Collaborate cross-functionally: translate business questions into analysis, present trade-offs clearly, and iterate with stakeholders and tech partners.
Deploy to production: partner with data engineers to ship to production (APIs, batch jobs, monitoring) and create feedback loops for continuous improvement.
Drive system improvements: propose pragmatic improvements to data, tooling, and process that reduce manual work and increase reliability.

Qualifications/Skills Required

Bachelors degree in Computer Science, Data Science, Statistics, or a related field.
3+ years of hands-on experience in data science, analytics, or ML, with at least one end-to-end project shipped to production used by real stakeholders.
Strong Python proficiency (Pandas, NumPy, Matplotlib, and Scikit-learn).
Sound understanding of ML fundamentals: problem framing, validation, metrics, overfitting, feature engineering.
Experience with SQL and NoSQL database structures along with relational, columnar and document databases.
Experience with LLMs and modern NLP: prompt engineering, retrieval-augmented generation (RAG), vector databases, knowledge graphs plus a pragmatic sense of when not to use them.
Strong data visualization and storytelling abilities using tools such as Plotly, Dash, Streamlit, or similar frameworks.
Familiarity with cloud services (AWS/Azure) for data storage, compute, and deployment.

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