Data Scientist

This is not a standard corporate data science role. The hiring organization is actively pivoting from a traditional, research-heavy model into a forward-thinking, analytics-first powerhouse.

As their foundational Data Scientist hire, the successful candidate will escape the burden of legacy systems and clunky technical debt. Instead, they will be given a clean, greenfield mandate to bridge the gap between academic-style macroeconomic/geopolitical research and production-grade machine learning. The chosen candidate will actively set the architectural and software engineering standards for the entire business line from day one.

How You’ll Make an Impact

• Build Greenfield Pipelines: Design, prototype, and scale end-to-end data pipelines and robust ML models to extract deep insights from massive structured and unstructured datasets.
• Drive NLP Innovation: Take ownership of a proprietary NLP data generation process, building and optimizing models for advanced risk analytics and generative AI applications.
• Be the “Translator”: Collaborate daily with the organization’s highly cross-functional team of Economists, Political Scientists, and Developers. The hire will transform abstract, non-technical research ideas into production code, and explain complex algorithmic outputs to non-technical stakeholders with clarity and ease.
• 3 to 7+ Years of Dedicated Data Science Experience: A proven track record delivering production-grade machine learning and NLP solutions. (Please note: The team is looking for hands‑on predictive modeling experts; general data analysts or loose titles will not pass their technical stages).
• A “Production-First” Mindset: A deep appreciation for software engineering fundamentals. The organization requires clean, long‑lived, maintainable enterprise code backed by unit testing, logging, exception handling, and strict version control. No messy, notebook‑only coders.
• Core Tech Stack Proficiency: High proficiency in Python or R, alongside standard packages (numpy, pandas, scikit-learn, pytorch).
• Cloud & MLOps Exposure: Familiarity with cloud platforms (AWS, Databricks, or Snowflake) and experiment tracking tools (MLFlow, Weights & Biases, or DVC).
• Strong Quantitative Foundations: A degree (ideally advanced) in Mathematics, Physics, Engineering, Data Science, or a highly quantitative field.

What’s in It for You

• True Career Mobility: Serve as the foundational anchor of an expanding team, giving the hire high internal visibility and a direct fast‑track toward future leadership.
• Compelling Package: A highly competitive salary base plus a discretionary target bonus.
• Continuous Learning: Access to dedicated training budgets, leadership development, and structural mentorship programs.

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