Product Developer – Commodities And Multi-Asset
London · Posted 1H AGO
Product Developer – Commodities And Multi-Asset
Location
London
Business Area
Product
Ref #
10054615
Bloomberg is a leading provider of financial benchmarks and custom index solutions. Strategy indices have become central to capital markets, powering ETFs, structured products, derivatives, and institutional mandates. As regulatory frameworks evolve and investor demand for systematic and rules-based solutions grows, we are expanding our Multi-Asset Product Development team.
This is an opportunity to join a highly collaborative and technically rigorous team within Bloomberg's Enterprise Data Products Group, focused on building scalable, systematic index solutions across asset classes.
What's the Role?
You will join Bloomberg's Multi-Asset Product Development team, where you will contribute to the design, backtesting, implementation and ongoing enhancement of Bloomberg's Multi-Asset indices, with a focus on the role of commodities within multi-asset allocation, risk-premia and QIS strategies.
This role sits at the intersection of research, product, and engineering. You will work on index and QIS methodologies on commodities but also spanning equities, rates, FX and credit ensuring they are scalable, robust, and aligned with both client needs and platform capabilities. You will help shape how commodity exposures, from carry and roll strategies to curve and trend premia, are built into diversified multi-asset and risk-premia products.
You will help support research-driven innovation, translate client requests into implementable index and QIS solutions, and partner with Engineering and Production teams to ensure strategies are automated and production-ready. You will also contribute to systematic data assessments, evaluating new datasets for fit-for-purpose use in benchmarks. This covers methodology, historical behavior, coverage and mappings, limitations of use, and the controls and platform requirements for onboarding. You will turn your findings into clear requirements that move through dedicated data working groups and into production. You will adapt your communication style to your audience and be equally adept at collaborating closely with colleagues across multiple teams, including Data, Enterprise Data and Risk and Compliance, as well as presenting to clients and soliciting feedback. You’ll be comfortable working with large and complex data sets, ideally with quantitative or data science skills (e.g., Python), and have a gift for extracting meaningful intelligence to drive innovation, product direction and data-driven decisions.
This role requires strong quantitative capability, technical fluency, commercial awareness, and a mindset focused on scalability and execution discipline. You'll be someone with the ability to approach challenges with creativity and enthusiasm; someone who has the courage to take positions advocating change and then deliver.
Regarding domain expertise, you'll understand trends in the index and QIS business, regulation, and what our customers need. You'll also be keen to learn more.
We'll trust you to:
You'll need to have:
We'd like you to have:
If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role. Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.
This is an opportunity to join a highly collaborative and technically rigorous team within Bloomberg's Enterprise Data Products Group, focused on building scalable, systematic index solutions across asset classes.
What's the Role?
You will join Bloomberg's Multi-Asset Product Development team, where you will contribute to the design, backtesting, implementation and ongoing enhancement of Bloomberg's Multi-Asset indices, with a focus on the role of commodities within multi-asset allocation, risk-premia and QIS strategies.
This role sits at the intersection of research, product, and engineering. You will work on index and QIS methodologies on commodities but also spanning equities, rates, FX and credit ensuring they are scalable, robust, and aligned with both client needs and platform capabilities. You will help shape how commodity exposures, from carry and roll strategies to curve and trend premia, are built into diversified multi-asset and risk-premia products.
You will help support research-driven innovation, translate client requests into implementable index and QIS solutions, and partner with Engineering and Production teams to ensure strategies are automated and production-ready. You will also contribute to systematic data assessments, evaluating new datasets for fit-for-purpose use in benchmarks. This covers methodology, historical behavior, coverage and mappings, limitations of use, and the controls and platform requirements for onboarding. You will turn your findings into clear requirements that move through dedicated data working groups and into production. You will adapt your communication style to your audience and be equally adept at collaborating closely with colleagues across multiple teams, including Data, Enterprise Data and Risk and Compliance, as well as presenting to clients and soliciting feedback. You’ll be comfortable working with large and complex data sets, ideally with quantitative or data science skills (e.g., Python), and have a gift for extracting meaningful intelligence to drive innovation, product direction and data-driven decisions.
This role requires strong quantitative capability, technical fluency, commercial awareness, and a mindset focused on scalability and execution discipline. You'll be someone with the ability to approach challenges with creativity and enthusiasm; someone who has the courage to take positions advocating change and then deliver.
Regarding domain expertise, you'll understand trends in the index and QIS business, regulation, and what our customers need. You'll also be keen to learn more.
We'll trust you to:
- Communicate complex quantitative concepts clearly to both technical and non-technical audiences
- Design, backtest, and validate multi-asset, risk-premia and QIS methodologies with commodity components
- Identify and mitigate backtesting biases (e.g., survivorship bias, look-ahead bias, overfitting)
- Source, validate, and map market data inputs across multiple systems, including commodity futures and curve data
- Evaluate technical constraints, automation requirements, and scalability early in the ideation process
- Partner closely with Engineering to implement and automate indices and strategies efficiently
- Work with Sales and clients to refine custom index and QIS requests, including commodity-linked requests, and assess feasibility
- Contribute to improving internal tooling and reusable index frameworks
- Support documentation and governance processes in line with regulatory standards
You'll need to have:
- A strong academic background in a quantitative discipline including, but not limited to, Finance, Engineering, Mathematics, Physics, Computer Science, Statistics, or Economics
- 5+ years of experience in index development, QIS, systematic strategies, quantitative analysis, or a related product development role
- Hands-on experience building and validating end-to-end systematic index backtests or quantitative strategy simulations
- Strong understanding of the commodities asset class, including futures term structure dynamics, roll yield, and sector-specific characteristics (Energy, Metals, Agriculture), and of how commodities behave within diversified multi-asset portfolios
- Strong understanding of multi-asset derivatives and systematic strategy design (e.g., futures, options, swaps), including risk-premia such as carry, trend, and curve
- Strong Python proficiency, with experience writing clean, modular, testable code
- Ability to independently prototype an index or strategy from an idea, to a validated backtest, to a product specification
- Experience working with large datasets
- Strong understanding of financial markets
- Familiarity with portfolio construction concepts and factor-based investing
- Solid understanding of backtesting methodology and common pitfalls
- Experience collaborating with Engineering teams to translate research into production systems
- Clear communication skills and the ability to translate client requests into structured implementation plans
- Working knowledge of benchmark regulations (e.g., BMR, IOSCO principles) and day-to-day governance determinations for index events
We'd like you to have:
- Experience with SQL and structured data environments
- Familiarity with performance optimization and vectorized computation
- Hands-on experience applying optimizers to portfolio construction (e.g., mean-variance, risk-budgeting, constrained optimization), including handling of constraints and turnover
- Exposure to QIS products distributed through banks, including familiarity with the structures and payoffs they are typically embedded in
- Understanding of how indices and QIS are monetized (ETFs, structured products, licensing)
- Experience with version control tools (e.g., Git)
If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role. Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.
