Data Scientist / AI Engineer

Teach the engine to reason, then ship it

At Fluxion, modeling and AI are the same problem.

You\'ll work on the core engine that evaluates, simulates, and reasons over complex graph-based business models. You\'ll also build the AI agents that use it to explore assumptions, propose scenarios, and explain what they find.

This is an end-to-end role. You will not hand a notebook to an engineer. You own the path from mathematical idea to production code, in the same codebase as everyone else.

This role sits at the intersection of:

• Applied mathematics and statistics

• Semantic and causal modeling

• LLM-based agents and reasoning systems

• Simulation and scenario analysis

• Production engineering

We are building infrastructure for reasoning over structured models, and the agents that reason with it.

What you\'ll do

• Design and implement the mathematical and statistical core of our modeling engine, covering forecasting, uncertainty, sensitivity, and causal propagation

• Turn modeling ideas into production code in the product codebase, not into prototypes someone else rewrites

• Build AI agents that operate on top of the engine: interpreting intent, constructing and modifying models, and running scenario exploration

• Design the interface between deterministic computation and probabilistic reasoning, deciding what the engine must guarantee and what the agent is allowed to infer

• Develop evaluation frameworks for agent behaviour, covering correctness, reliability, regression, and failure analysis

• Make the output explainable, with traceable assumptions, visible causality, and defensible numbers

• Work directly with users\' real modeling problems and translate them into engine capabilities

About you

• Come from a quantitative background (data science, mathematics, statistics, physics, operations research, or similar) and have since moved into building AI systems

• Are a genuinely end-to-end data scientist: you ship, review, test, and maintain code that runs in production

• Have strong foundations in probability, statistics, optimisation, and numerical methods, and know when a model is wrong rather than merely unconverged

• Have built with LLMs beyond prompting, including tool use, structured output, agent orchestration, retrieval, and evaluation

• Think rigorously about correctness and determinism, and are uncomfortable shipping a system whose failure modes you cannot describe

• Can reason about the semantics of a problem and not just the metrics, about why a relationship holds and not only that it correlates

• Communicate clearly about uncertainty with people who need to make decisions under it

Our relevant tech stack for this role

• Python

• Polars, NumPy, SciPy

• PostgreSQL, SQLAlchemy

• FastAPI

Additionally, experience with any modeling packages, such as scikit-learn, Prophet, XGBoost etc. and any AI agent orchestration and evaluation frameworks is beneficial and potentially applicable.

Strong plus signals

• Background in Computer Science, Mathematics, Physics, Statistics, or a similar quantitative field

• Experience with financial modelling, forecasting, optimisation, planning, or simulation systems

• Experience designing evaluation harnesses for LLM or agent systems

• Experience with causal inference, Bayesian methods, or probabilistic programming

• Experience with graph-based representations, constraint systems, or symbolic computation

• Have built a product feature end to end, not only analysis or research output

• Have shipped work where you both derived the method and wrote the production code behind it

• Prefer designing the system underneath the pipeline to tuning the pipeline that already exists

• Are at home in problems that are not yet well specified, on a stack that is still changing

About Fluxion

Fluxion is a decision intelligence platform that empowers forward-looking teams to answer their toughest "what-if" questions with confidence. Using AI-native scenario modeling, Fluxion lets organizations explore assumptions and simulate outcomes across financial and operational models in real time — without rebuilding spreadsheets or rigid tools. Its semantic approach to decision modeling keeps assumptions explicit, preserves causality, and delivers traceable, explainable insights so teams make better decisions faster. Designed for high-impact decisions under uncertainty, Fluxion replaces fragile, manual exploration with a dynamic environment where curiosity is cheap, safe, and continuous.