Data Scientist

Who are we?

Finance teams need clear visibility into their cash flow, but most still rely on Excel to manually track data across multiple bank accounts. This makes it hard to see real-time cash positions, spot trends, and drive smarter financial decisions.

Nilus changes that.

We're building an AI-powered cash management platform that gives finance teams real-time cash visibility, forecasting, and reconciliation—all powered by AI, eliminating manual work and unlocking insights that help businesses grow.

Backed by top VCs like Bessemer, Felicis, BTV, and Symbol, Nilus is a fast-growing fintech startup led by a team of ex-fintech pros passionate about transforming the finance function.

What We’re Looking For:

We’re looking for a Data Scientist to join our growing AI team. You’ll work on experimenting and testing ML and LLM solutions that drive real value for finance professionals. As part of a cross-functional team, you’ll work closely with product managers and engineers to deeply understand customer and product needs, identify pain points, and develop AI-driven solutions that are both effective and scalable.

What you’ll be doing:

Conduct research and experiments on ML and LLM models, iterating on solutions to enhance financial predictions and analytics.
Design and implement statistical models and machine learning pipelines to solve real-world financial problems.
Work with structured and unstructured financial data, leveraging Python, SQL, and LLM tools.
Evaluate model performance, present findings to stakeholders and decision-makers, and recommend improvements.
Build proof-of-concepts and help transition successful models into production.

Your qualifications:

Bachelor's degree in a quantitative field (Statistics, Computer Science, Data Science, or related).
4 years hands-on academic or industry experience in research and experimentation of ML/DL/AI models.
Proficiency in Python (Pandas, NumPy, Scikit-learn, etc.), strong SQL skills, familiarity with LLM tools & APIs.
Strong statistical foundations and ability to translate theoretical methods into real-world applications.
Ability to clearly measure, explain, and present model performance to decision-makers, offering alternatives when necessary.
Experience in finance or financial data analysis (a plus).

If you are excited about this role and think you would be a great fit, we'd love to hear from you!