Product Manager

DRW
London
1 year ago
Applications closed

Related Jobs

View all jobs

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Manager

Job LocationLondonEmployment typeRegularDepartmentTradingTargeted Start DateImmediate

DRWis a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. 

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. 

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

DRW is looking for aProduct Manager to join its Commodities trading group to oversee and manage our tools used to support research and trading across many commodity products. You will manage the product needs of many commodities trading desks and be responsible for the successful implementation and delivery of new features on existing tools as well as new product initiatives. You will work closely with Data Engineers and Software Engineers to execute both strategic plans and tactical needs to manage our internal tools.

What You Will Do

Be an expert in trading and research workflows and identify ways to streamline and enhance those workflows to build best in class research and trading tools. Work day-to-day with Data Engineers and Software Engineers to manage live projects, identify roadblocks and risks, and ensure the team can operate efficiently. Work with the Commodities Engineering technical leads and executive management to communicate data initiatives and ensure the team is fully staffed and appropriately set up to support our product needs. Constantly evaluate our tools and keep abreast of new market offerings from vendors and internal teams and ensure we have the best tools in the industry and evaluate build/partner/buy opportunities to manage our time to market and scalability. Constantly evaluate our data sets and keep abreast of new market offerings from vendors and internal teams and ensure we have the best foundational data sets in the industry. Run communication and education initiatives to ensure all our teams know what data sets and analytical tooling we have and how they can use them. Liaise across DRW infrastructure groups such as our procurement, central engineering and data, database administration and cloud computing teams to communicate about our initiatives and ensure we have support and capacity from those teams to execute effectively.

What you bring to the team

Experience as a product/project/program manager in a technical role, managing software engineers and data analysts. Excellent communication skills and experience managing cross-functional teams and large sets of stakeholders. Experience with agile software development projects and fast-paced, iterative delivery. Experience in helping to design and implement a technology stack for data management purposes a plus. Experience working in a commodity trading house or hedge fund highly preferred. A passion for planning, organization and skills using technical documentation and planning tools such as Wikis and JIRA. Familiarity with UI/UX design tools. Familiarity with cloud computing and large-scale data warehouse implementation. Familiarity with SQL database usage and design. Familiarity with Python.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.