At a Glance
- Tasks: Lead a data squad to enhance product flows and partner integrations.
- Company: Join Reflexivity, an innovative AI-native investment analysis platform.
- Benefits: Competitive salary, dynamic work environment, and opportunities for growth.
- Other info: Exciting chance to work with major partners and shape the future of financial data.
- Why this job: Make a real impact in the finance tech space with cutting-edge AI tools.
- Qualifications: 3-5 years in PM or technical roles, strong communication and technical skills.
The predicted salary is between 50000 - 80000 £ per year.
About Reflexivity
Reflexivity builds an AI-native investment analysis platform for institutional investors, combining trusted financial data, knowledge graphs, document intelligence, and explainable AI to surface actionable insights instead of noise. Alfred, our financial reasoning engine, helps investment teams move from question to evidence-backed analysis faster - across research, screening, portfolio insights, scenario analysis, and partner integrations.
Why this role exists
Our data squad sits at the center of some of the company's most important relationships with major partners. Reflexivity consumes partner data across pricing, M&A, corporate events, fundamentals, ownership, news, and text documents - and also packages Reflexivity capabilities back into partner products, improving their surfaces with the intelligence we have built. The PM who built this motion is leaving for business school. We are looking for a sharp, technically fluent product owner to take it over, raise the bar, and keep the system scaling.
What you'll own
- You will lead the data squad - four engineers, two Python and two Golang - and act as the day-to-day product owner for the data and product flows between Reflexivity and major partners.
- The work splits roughly two ways, and today it leans outbound:
- Outbound: Take capabilities built inside Reflexivity and ship them into partner products. You will work closely with partner product and engineering teams to decide what to integrate, map their constraints to ours, and get production-grade functionality live inside someone else's environment.
- Inbound: Keep refining how Reflexivity ingests, models, and uses partner data on our own platform. You will own data-model mapping, business logic, and the QA bar.
A typical week
- Run a working session with a partner engineering team to align on schema mapping for a new dataset.
- Write a crisp spec for engineers on a corporate-actions edge case.
- QA last week's release against ground truth and decide what ships versus what holds.
- Partner with GTM on how to explain a coverage universe to clients.
- Use AI tooling such as Cursor, Claude, or Windsurf to prototype business logic before handing it to engineering.
- Make a judgment call on whether to push back on a partner ask or absorb it into the roadmap.
What we're looking for
- 3-5 years as a PM, TPM, or technical/data role with PM-shaped responsibilities. We do not need senior; we need sharp.
- Genuine technical fluency. You can read schemas, reason about APIs and data pipelines, talk to engineers as peers, and write specs that backend engineers can execute without multiple clarification rounds. You will not write production code.
- Comfort running external partnerships. You can lead a working session with another company's team and walk out with decisions, not vague action items. You can read the room when their internal constraints or politics are affecting the work.
- High tolerance for ambiguity. Financial data has a long tail of odd business rules and undocumented edge cases. You should enjoy chasing them down rather than waiting for someone else to define them.
- Daily user of AI assistants. You should already use Cursor, Claude, Windsurf, or similar tools to prototype logic, explore data, and codify business rules - not just to write emails. This is how the team works.
- Strong written communication. Specs, partner-facing docs, internal updates, release notes - the role is half writing.
- A QA mindset. You think about how systems break before they break, and you build the muscle to catch regressions early.
Nice to have
- Background in financial data - market data, fundamentals, corporate actions, ownership, news, research, or alternative data from providers such as Bloomberg, FactSet, S&P Global, Moody's, ICE, Nasdaq, Cboe, or similar.
- Experience as a data or technical PM at an early-stage startup, where the role spans well beyond its formal description.
- CS, math, finance, or quantitative degree - or a self-taught track record that proves the same thing.
Salary Range: £50,000 - £80,000 GBP
Product Manager, Data, London employer: Reflexivity
Reflexivity is an exceptional employer that fosters a dynamic and innovative work culture, particularly for the Product Manager role in London. With a focus on cutting-edge AI technology and strong partnerships, employees benefit from collaborative teamwork, opportunities for professional growth, and the chance to make a significant impact in the financial data landscape. The company values technical fluency and encourages the use of AI tools, ensuring that team members are equipped to thrive in a fast-paced environment while contributing to meaningful projects.
StudySmarter Expert Advice🤫
We think this is how you could land Product Manager, Data, London
✨Tip Number 1
Network like a pro! Reach out to people in your industry on LinkedIn or at events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Prepare for those interviews! Research the company and its products, especially how they use data. Show them you’re not just a fit for the role, but also passionate about what they do.
✨Tip Number 3
Practice your pitch! You need to communicate your experience and skills clearly. Think of it as telling a story that highlights your journey and how it aligns with the job.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step!
We think you need these skills to ace Product Manager, Data, London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Product Manager role. Highlight your technical fluency and experience with data, as these are key for us at Reflexivity. Show how your skills align with our mission of delivering actionable insights.
Showcase Your Writing Skills:Since strong written communication is crucial for this role, ensure your application is clear and concise. Use bullet points where necessary and avoid jargon. We want to see that you can write specs and partner-facing documents effectively.
Demonstrate Your Problem-Solving Ability:In your application, share examples of how you've tackled ambiguity or complex problems in past roles. We love candidates who enjoy chasing down odd business rules and can think critically about data workflows.
Apply Through Our Website:We encourage you to apply directly through our website. This helps us keep track of applications and ensures you’re considered for the role. Plus, it’s a great way to show your enthusiasm for joining our team!
How to prepare for a job interview at Reflexivity
✨Know Your Data Inside Out
As a Product Manager for data, it's crucial to have a solid grasp of the financial data landscape. Familiarise yourself with key concepts like schemas, APIs, and data pipelines. Be ready to discuss how you would approach integrating partner data and refining data models.
✨Showcase Your Technical Fluency
You’ll need to communicate effectively with engineers, so brush up on your technical jargon. Prepare to explain how you’ve previously collaborated with engineering teams and how you can write clear specs that require minimal clarification. This will demonstrate your ability to bridge the gap between technical and non-technical stakeholders.
✨Prepare for Partnership Scenarios
Since this role involves working closely with external partners, think about past experiences where you've led discussions or sessions with other teams. Be ready to share examples of how you navigated constraints and made decisions that benefited both parties. This will show your comfort in managing external relationships.
✨Embrace Ambiguity and Problem-Solving
Financial data often comes with its quirks and edge cases. Prepare to discuss how you tackle ambiguity and chase down complex problems. Share specific instances where you identified issues before they became bigger problems, showcasing your proactive QA mindset.
