You will write code, direct coding agents, review their output, and take responsibility for the model logic and evaluation evidence.
What you'd work on
Choose methods across statistics, econometrics, mathematical optimization, scenario modeling, geospatial science, weather and climate models, machine learning (for example, boosting), deep learning, foundation model fine-tuning and training, and decision models. Connect the quantitative work to agent harnesses, reusable agent skills, and Model Context Protocol (MCP) servers. Choose the simplest method that meets the evaluation requirements.
Build and extend our forecasting stack, which combines statistical baselines, fine-tuned time-series foundation models, and multi-model pooling behind MCP tools, with leakage-safe walk-forward backtests and probabilistic metrics. Design recursive self-improvement (RSI) loops in which agents propose, test, and keep or revert changes to foundation model tuning using validation data, with untouched test data reserved for final evaluation.
Turn forecasts into decisions: formulate and solve optimization problems (for example, a gas-purchase mixed-integer program, and candidate demonstrators for ancillary-service offers, congestion-rights auctions, and margin and collateral planning) and wire them into agent workflows that traders can use.
Build and evaluate decision models for text-heavy problems such as counterparty screening and news signals. Compare approaches using encoder classifiers, natural language inference classifiers, small language models, and large language models (LLMs), with calibration, deferral to human review, and held-out evaluation.
Write quant code designed to be called by agents: typed inputs, explicit units and trading calendars, reproducible calculations, controlled randomness where needed, and tests that expose errors. Package it as MCP tools and reusable agent skills.
Own the quant evaluation gate inside our agent harnesses, so that agent-written research passes checks for leakage, baseline parity, units, calendars, and out-of-sample performance before anyone trusts it.
Run hybrid agentic and human codebase reviews: use review agents to sweep code for defects, leakage, and test gaps, then apply human judgment to model logic, market conventions, and what is ready to ship.
Lead client project teams of domain experts, quants, full-stack engineers, and analysts from problem framing through delivery: sit with client trading, origination, and risk teams, turn loosely framed problems into working, evaluated assets quickly, and explain to senior stakeholders what a model can and cannot be trusted to do.
Lead go-to-market initiatives with practice leadership: shape offerings, build demonstrators, contribute to proposals, and present to client trading and risk leaders.
Coach teammates and set review standards for quantitative work across the team.
Early priorities could include taking a trading demonstrator from prototype to a backtested result that meets agreed evaluation requirements; moving a decision model prototype (for example, counterparty screening) from synthetic benchmarks onto a realistic held-out evaluation; and adding commodity-specific checks to the shared research and verification workflows that our quants, full-stack engineers, and analysts build on.
What good looks like
Treat the Must-have list below as the minimum screening bar for the role. The Nice-to-have list is additive, not required.
Must-have
Bachelor's degree in mathematics, statistics, physics, engineering, computer science, economics, finance, operations research, or a related quantitative field. An associate degree is acceptable with at least 2 additional years of directly relevant experience and a strong record of applied quantitative work.
Minimum 5 years of experience in a quantitative role where your models fed trading, hedging, pricing, or risk decisions: for example on a commodity, energy, rates, foreign exchange (FX), credit, equity, options, or crypto desk, or in a systematic trading, structuring, or front-office strategy team.
Hands-on experience in at least one of: derivatives pricing and hedging, forward-curve or volatility modeling, price or demand forecasting, portfolio or asset optimization, or systematic strategy research.
Rigorous research practice you can show: walk-forward or out-of-sample backtests against explicit baselines, leakage control, and honest reporting of results that did not hold up.
Hands-on experience formulating and solving mathematical optimization problems (for example, linear, mixed-integer, stochastic, or robust optimization) tied to commercial or operational decisions.
Expert Python for quantitative work, including tabular data processing, high-performance central processing unit (CPU) and graphics processing unit (GPU) compute, optimization solvers, and statistical, machine learning, deep learning, foundation model, generative modeling, and geospatial packages, with Git-based collaboration, tests, and code review.
Current, daily practice building with coding agents such as Claude Code or Codex: you direct agents to write, test, and refactor research code, you catch and fix what they get wrong, and you have built or extended at least one agent workflow, tool, or skill that other people or agents use.
Hands-on experience applying modern model classes to quantitative problems, including machine learning, deep learning, foundation models, LLMs, small language models, encoder classifiers, and decision models. You have fine-tuned or adapted at least one of them and measured whether the gain held up out of sample against a classical baseline.
Evidence that you can lead technically: set direction on a problem, lead a team or workstream, and review others' work rigorously (including agent-written code).
Evidence of strong communication: you have presented quantitative work to traders, clients, or senior stakeholders and can explain model logic and its limits clearly and persuasively, in writing and in person.
Nice-to-have
Commodity or energy trading experience, especially power, gas, LNG, oil products, freight, or agriculture, including physical constraints such as storage, transport, delivery calendars, and settlement conventions.
Master's or PhD in a quantitative field, or a master's in financial engineering or operations research.
Experience with foundation model fine-tuning (including parameter-efficient methods) and foundation model training on market or operational data, plus hyperparameter search.
Experience with scenario modeling, geospatial data, or weather and climate models as inputs to market or operational decisions.
Experience building MCP servers, agent skills, or evaluation harnesses, or with agent frameworks and software development kits (SDKs).
Experience with model calibration, deferral rules, and benchmark design for LLM or classifier systems.
Mathematical optimization running in production, used by traders, schedulers, or planners.
Experience with RSI loops in which agents propose, evaluate, and keep or revert changes to model tuning, prompts, or harnesses using validation benchmarks, with separate final test data and safeguards against overfitting and reward hacking.
Familiarity with accelerator compute stacks such as NVIDIA CUDA, AMD ROCm, or Apple MLX, including GPU training workflows, model serving, or research infrastructure.
Familiarity with cloud platforms such as Amazon Web Services (AWS) and/or Microsoft Azure for deploying quantitative workloads, model services, or research infrastructure.
Client-facing consulting, forward-deployed, or desk-embedded delivery experience, including leading client project teams or contributing to proposals and business development.
Team and environment
QuantAI is a small, hands-on global team focused on commodity and energy markets within Accenture's global Commodity Markets practice. Strong work is seen by senior and executive stakeholders quickly.
You will work alongside commodity domain experts, fellow quants, full-stack engineers, and analysts around the world. Domain experts frame client and market needs, and full-stack engineers build the product surface, MCP services, and their integration with our agent harnesses. Your job is to make the quantitative core as strong as the domain expertise and engineering around it.
We build with agents by default: shared workflow skills for research, verification, and benchmarking; curated knowledge bases on quantitative methods, forecasting, and commodity markets; and harnesses that run review and repair loops on agent-written code.
Most of our trading assets are demonstrators moving toward repeatable client offerings. You will help decide which ones earn that step, based on evidence.
The team works globally with clients and colleagues across time zones, with travel to client sites when the work requires it.
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities. Visit us at www.accenture.com
Equal Employment Opportunity Statement
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