Senior Associate, Data Scientist
Confirmed on the employer's own hiring board on Oct 4, 2026. First seen by Alion on Sep 17, 2026. BNY scores A on the Alion truth index.
Overview
This is a senior hands-on engineering role focused on building and scaling AI-powered solutions for Wealth & Investment Management. You will work at the intersection of investment expertise, quantitative analysis, and modern AI technologies to create intelligent applications that enhance portfolio construction, investment research, advisor productivity, and client outcomes.
The ideal candidate combines strong mathematical and analytical capabilities with a passion for software engineering and applied AI. You will partner with investment professionals, product leaders, data scientists, and engineers to transform complex financial challenges into innovative, production-grade AI solutions.
In this role, you'll make an impact by:
Designing and developing AI-powered applications, intelligent workflows, and agent-based solutions that support Wealth & Investment Management business objectives.
Building portfolio analytics, investment research, and decision-support solutions leveraging market, client, and third-party data sources.
Integrating and operationalizing data from multiple investment platforms, market data providers, and financial systems to create scalable AI-ready data products.
Developing APIs, data pipelines, automation frameworks, and reusable software components that accelerate solution delivery.
Applying quantitative and statistical techniques to support portfolio construction, risk analysis, performance attribution, and investment insights.
Assisting in the implementation of retrieval, embedding, semantic search, and knowledge-driven capabilities to enhance investment workflows.
Supporting testing, model evaluation, monitoring, and operational activities to ensure solution quality, accuracy, and reliability.
Contributing to shared frameworks, engineering standards, reusable assets, and technical documentation across the AI Garage.
Participating in code reviews and adopting engineering best practices to promote maintainability, scalability, and security.
Continuously exploring emerging AI, data science, and financial technology innovations to drive differentiated business value.
To be successful in this role, we're seeking the following:
4-7 years of experience in software engineering, quantitative analytics, financial technology, data engineering, AI engineering, or a related field.
Strong mathematical, statistical, and analytical foundation with the ability to apply quantitative concepts to real-world investment and wealth management challenges.
Practical experience with portfolio construction, investment analysis, asset allocation, risk modeling, investment research, or related Wealth & Investment Management disciplines.
Strong programming skills in Python and proficiency with data analysis libraries and software engineering best practices.
Experience working with financial data sets, investment platforms, market data vendors (FactSet, Bloomberg, Morningstar, or similar), and portfolio analytics solutions.
Familiarity with cloud-native development, APIs, databases, and modern software development lifecycle practices.
Exposure to Generative AI, machine learning, agentic applications, retrieval-augmented generation (RAG), or advanced analytics solutions.
Hands-on experience developing data pipelines, integrating third-party data sources, and building scalable analytical applications.
Strong problem-solving and critical-thinking skills with the ability to translate complex business requirements into practical technical solutions.
Excellent communication and collaboration skills with the ability to work effectively across investment, product, and engineering teams.
Intellectual curiosity and a passion for applying emerging AI technologies to solve complex financial and investment challenges.
Preferred Qualifications
Experience supporting portfolio management, investment advisory, model portfolio, or wealth management platforms.
Knowledge of optimization techniques, quantitative finance, portfolio construction methodologies, and risk analytics.
Practical implementation experience with agentic AI solutions, MCP/A2A integration patterns, and automated evaluation and validation frameworks.
Experience building production-grade AI applications in regulated financial services environments.