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.
You May Be a Good Fit If You Have... 🤔
- 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.