AI Research Analyst (AI & Digital Asset Solutions)

At Franklin Templeton, we believe success is built through powerful partnerships. As a forward thinking asset manager, we build dynamic relationships with clients, understand their goals, and navigate complex markets together. We leverage cutting edge strategies and deep insights to unlock opportunities for long term wealth creation. Our talented, global teams bring expertise that is both broad and unique. From our welcoming, inclusive, and supportive culture to our globally diverse business, we offer opportunities not only to help you reach your potential, but also to contribute to our clients’ success. About AI & Digital Asset Solutions Financial markets are evolving at a breakneck pace—and so are the capabilities required to stay ahead. Investment teams are increasingly asking two questions: how to apply frontier AI to real investment problems (alpha generation, portfolio construction, risk decomposition, workflow automation), and how to rigorously evaluate digital assets alongside traditional asset classes. AI & Digital Asset Solutions is a newly formed, entrepreneurial research team within Franklin Templeton Investment Solutions (FTIS) focused on answering both. We build practical tools, models, and frameworks that investment teams can use—bridging cutting‑edge research and real‑world deployment. On the AI side, we design and implement AI‑enabled capabilities across the investment lifecycle for FTIS —from agentic workflows and research copilots to enhancements in portfolio construction and client‑facing tools—working closely with investment business partners, technology, and central AI teams. As an AI Research Analyst, you will get early exposure to solving real problems at the intersection of the two most exciting forces reshaping finance. Responsibilities Work with portfolio management, research, and business development teams to identify opportunities where AI can enhance alpha generation, portfolio construction, risk management, predictive analytics, and client‑facing workflows Translate ambiguous investment problems into well‑scoped technical solutions, accounting for real‑world constraints, edge cases, and data limitations Work with central AI and engineering teams to productionize models and deploy them into investment workflows Help drive adoption of AI tools by communicating results clearly and enabling investment teams to use what you build Contribute to defining and articulating a firm‑wide AI strategy for investment management, including identifying high‑impact use cases, establishing best practices, and driving adoption across teams Qualifications 2+ years of experience in an applied quantitative field, ideally involving deploying DL/ML within an investment management organization Advanced degree (MS or PhD) in a technical field such as computer science, machine learning, statistics, mathematics, or a related discipline preferred but not required Working knowledge of financial markets and investment concepts (e.g., portfolio construction, asset allocation, risk management) is a plus, but not required Strong programming skills in at least one modern language (e.g., C++, Python, Java, C#, JavaScript), with the ability to quickly learn new languages and frameworks as needed Experience developing or applying machine learning models (e.g., deep learning, time series modeling, or optimization techniques) in real‑world settings High intellectual curiosity and an entrepreneurial mindset, with a proven ability to quickly learn new concepts and apply them to ambiguous, real‑world problems Demonstrated experience working on open‑ended, ambiguous problems, with the ability to independently scope questions, design approaches, and iterate toward practical solutions Proven experience owning end‑to‑end development of data or AI solutions, from initial concept and modeling through production deployment and real‑world adoption Strong communication skills with the ability to explain complex quantitative concepts to non‑technical stakeholders When Applying Please upload your GitHub repository, along with your resume. Work Schedule & Location Hybrid work schedule Primary Work Location: Boston‑MA Secondary Work Locations: San Mateo‑CA, San Ramon‑CA, Pasadena‑CA Compensation Range #J-18808-Ljbffr