The role:
QuantAI is building cutting-edge AI-native decision systems for energy, commodities, financial, trading, and industrial operations, where the economics and mechanics are equally real. We are looking for a hands-on quantitative lead who can identify where advanced modeling creates real value, prove it through rigorous research, and help turn the strongest ideas into durable, reusable products.
This is a player-coach role. The person in this role will set quantitative direction, stay close to the code and research process, and help decide what should become a demo, a pilot solution, or a reusable client offering. You will also help develop and set the bar for a small high-bar team spanning quantitative and engineering talent. This is not a generic analytics manager, a pure advisory lead, or a research role that stops at notebooks. The bar is work that can stand up to backtests, operational constraints, desktop or cloud delivery realities, and client scrutiny.
The Work:
- Frame high-value quantitative problems across forecasting, optimization, quantitative risk, anomaly detection, and related decision workflows tied to economic or operational outcomes.
- Personally prototype models, run backtests, simulation studies, and solver-based experiments, and set the research bar for evaluation, benchmarking, and out-of-sample discipline.
- Decide when modern sequence modeling, representation learning, generative modeling, optimization, or hybrid systems materially outperform classical methods, and when they do not.
- Bring solver-based optimization, physical-system modeling, and market-structure reasoning closer to the foreground when the mechanics of the system matter as much as predictive accuracy.
- Partner with engineers to turn strong research into reliable, auditable tools, whether that means cloud-hosted services, client-ready interfaces, or packaged desktop workflows.
- Help keep the team's first commercial wedge sharp around energy, commodities, and industrial decision systems, while still supporting adjacent financial and trading workflows where the fit is real.
- Translate quantitative work for senior stakeholders when the model, decision logic, and business consequence all need to be clear at the same time.
- Travel - as needed, up to 25%
Team and environment
- QuantAI is small by design and sits between quantitative research, product engineering, and client delivery inside Accenture. Strong work gets recognized by senior stakeholders quickly.
- The team is deliberately focused: advanced algorithms wrapped in enterprise-grade workflow, governance, and packaging for high-stakes decision systems.
- The team is building reusable assets that can move from demo to pilot to repeatable client offering, not a collection of disconnected proofs of concept.
- You will work with technical peers who bring different strengths: deep quantitative research on one side, and product or agentic engineering on the other. The goal is to combine those strengths into something clients will trust, buy, and rely on.
- This role is hands-on and calls for steady judgment in ambiguous territory. Influence, judgment, and follow-through matter more than acting as a layer above the technical work.
Here's what you need:
- Bachelor's degree in mathematics, statistics, engineering, computer science, economics, finance, operations research, or a related field. An associate degree is acceptable with at least a minimum of 2 additional years of directly relevant experience and a strong record of applied quantitative work.
- Minimum 5 years of experience in quantitative modeling, decision science, analytics consulting, quantitative research, or related product development roles where model output influenced real decisions.
- Minimum 3 years of hands-on experience in one or more of the following areas: forecasting, optimization, quantitative risk, anomaly detection, market modeling, physical-system modeling, or related high-stakes decision systems.
- Minimum of 2 years of experience in power, energy, commodities, utilities, financial, trading, market operations, industrial systems, or a related field.
Bonus points if you have:
- Strong Python and hands-on experience with modern quantitative research workflows, including rigorous back testing, simulation, benchmarking, and evaluation design, plus enough practical depth to guide deep-learning or optimization work when it materially outperforms simpler methods.
- Evidence that you can operate as a hands-on technical lead: set direction, review work rigorously, work closely with product engineers, translate model logic for senior stakeholders, and decide what should be productized next.
- MBA, master's in financial engineering, operations research, applied mathematics, or a related advanced degree.
- Exposure to cutting edge large foundational model training and finetuning techniques or agent-assisted workflows when they improve the decision system rather than distract from it.
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