Quant Researcher - Statistical Arbitrage

About the role

In this role, you own the end to end design of statistical arbitrage experiments that convert raw market data into robust, rule based trading signals. You are responsible for turning noisy, high dimensional data into clean, decision ready inputs that feed directly into execution logic. Your work directly determines how systematic strategies behave across varying market conditions, influencing both risk exposure and realized performance. You collaborate with traders and engineers to ensure that research insights are implementable, scalable, and aligned with business objectives. You maintain a rigorous feedback loop where empirical results challenge assumptions, and models are refined or retired based on evidence. You document methodologies and findings so that other researchers can reproduce your work and build upon it efficiently. You are expected to question every assumption in the analysis, from data selection to performance attribution, and to defend your choices with clear reasoning. You act as a gatekeeper for signal quality, ensuring that only well tested and transparent ideas move from research to production.

Key facts

What you'll do

You will interrogate historical and live market data to uncover persistent inefficiencies that can be expressed as systematic trading rules. You will construct and maintain datasets that merge alternative data sources with standard market metrics, ensuring integrity, coverage, and low latency where relevant. You will prototype models and statistical tests, comparing theoretical distributions against observed market behavior to identify exploitable signals. You will design performance metrics that isolate true skill from random variation, enabling clear comparison across strategies and time periods. You will run controlled simulations and backtests, diagnosing edge cases, overfitting risks, and data leakage before any deployment proceeds. You will translate complex analytical outputs into concise narratives that highlight risk, opportunity, and the reasoning behind each decision. You will partner with engineers to operationalize research, defining interfaces, data pipelines, and monitoring that keep models reliable in production. You will continuously review live performance, updating assumptions and recalibrating parameters as market regimes shift over time. You will mentor junior analysts on best practices for data handling, visualization, and clear communication of analytical results.

Requirements

You possess a proven ability to work with large datasets and trading infrastructure, handling market scale information without performance degradation. You have applied experience through statistical arbitrage or analogous research roles in similar domains, demonstrating familiarity with finance specific constraints and nuances. You hold a Bachelor's degree or higher, with a strong quantitative background that supports advanced modeling and rigorous hypothesis testing. You are comfortable navigating incomplete or messy data, applying sound judgment to clean, transform, and validate inputs before analysis begins. You can manage multiple analytical tasks simultaneously, maintaining accuracy and focus when under tight time constraints and high stakes. You communicate complex ideas clearly, balancing technical depth with concise explanations tailored to different audiences. You write maintainable, well structured code, using version control and testing practices that support collaboration and long term reproducibility. You show intellectual curiosity, actively seeking out new methods, tools, and data sources that could enhance your research edge.

Practical notes

Typical interview steps

Analyst interviews often include a SQL or spreadsheet exercise, a case question, and behavioral rounds. Candidates may be asked to analyze a dataset, define a metric, or estimate an outcome. Presenting findings clearly is tested as often as the analysis itself. Interviewers often test speed and clarity with a timed exercise. Explaining what the numbers mean, not just what they are, is the differentiator.

Good to know

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Analyst careers can grow into senior analyst, analytics manager, or data science roles. Some analysts move into product or business operations. Deeper technical skills or broader business ownership are the two main paths. Analyst roles are a common on ramp into product, marketing, or operations leadership. Building deep domain knowledge alongside analytics is the fastest path.

About the company

Amber Group builds systems that turn market data into structured decision inputs. The team designs workflows where signals, models, and execution align around clear rules. Work happens in cycles of testing, measurement, and adjustment. You refine logic, observe outcomes, and iterate. To contribute, open the careers section on our site, review current openings, and submit an application through the listed path.