QUANT ANALYST

Responsibilities

As an Algo Quant Analyst supporting Bernstein SG’s electronic execution activities, you will conduct quantitative research and analysis on individual orders, order sets, execution strategies and market structure. You will translate analytical findings into client-consulting materials, performance insights, research publications and practical improvements to TCA and order-monitoring capabilities. The role is expected to actively leverage and contribute to AI-powered workflows, automation initiatives and data-driven decision-support capabilities that enhance Bernstein SG’s execution platform and client experience.

Your main responsibilities will include:

·       Conduct quantitative research on individual orders and sets of orders for client consulting and presentation.

·       Analyse execution performance and provide intraday benchmark feedback using relevant metrics and market context.

·       Research market structure topics and contribute to Whitebooks, Spotlights and analytical publications.

·       Design quantitative research for new algorithmic execution strategies.

·       Analyse existing strategies to support optimisation and fine-tuning.

·       Develop analytical tools and systems for transaction-cost analysis and order monitoring.

·       Assess implementation shortfall and analyse VWAP/TWAP slippage, including market-impact and timing components.

·       Support on-demand mid-order reforecasting using updated market conditions.

·       Document methodologies, assumptions, outputs and controls.

·       Comply with applicable risk, conduct, confidentiality and internal-control requirements.

Profile required

·       Strong quantitative background in mathematics, statistics, econometrics, financial engineering, computer science or a related discipline.

·       Good understanding of equity market microstructure, electronic trading and algorithmic execution.

·       Experience analysing execution benchmarks, transaction costs, liquidity and market-impact drivers.

·       Programming and data-analysis capability using Python, SQL or comparable approved technologies.

·       Strong knowledge of data validation, back-testing principles, model assumptions and analytical controls.

·       Ability to communicate complex findings clearly to technical and non-technical stakeholders.

·       Strong understanding of artificial intelligence and machine-learning concepts applicable to trading, market microstructure, forecasting and execution analytics.

·       Ability to use AI-assisted tools to accelerate quantitative research, statistical analysis, feature exploration and insight generation.

·       Experience evaluating AI-generated analytical outputs through quantitative testing, back-testing and statistical controls.

·       Familiarity with predictive modelling, time-series analysis and AI-assisted forecasting approaches.

·       Ability to integrate large structured and unstructured datasets into controlled AI-enabled analytical workflows.

·       Experience with Python-based data-science and machine-learning ecosystems and model-performance assessment.

·       Understanding of explainable AI, model validation, bias detection, data lineage and AI governance.

·       Ability to translate emerging AI capabilities into practical use cases enhancing TCA, algo optimisation and client consulting.