At a Glance
- Tasks: Lead a data squad to integrate and enhance partner products with AI-driven insights.
- Company: Join Reflexivity, an innovative AI-native investment analysis platform.
- Benefits: Competitive salary, dynamic work environment, and opportunities for professional growth.
- Other info: Work with cutting-edge AI tools and collaborate with major financial partners.
- Why this job: Make a real impact in the finance sector by driving data integration and product innovation.
- Qualifications: 3-5 years in product management or technical roles with strong data fluency.
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, the majority of the role today: 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. Near‑term examples include ingesting MCP servers, moving select feeds from APIs to FTPs, sharpening entity resolution and coverage universes, and continuing to find efficiencies in high-volume data workflows.
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.
£50,000—£80,000 GBP
Product Manager, Data, London employer: Reflexivity
At Reflexivity, we pride ourselves on fostering a dynamic and innovative work culture that empowers our employees to excel in their roles. As a Product Manager in our London office, you will have the opportunity to lead a talented data squad, collaborate with major partners, and drive impactful projects that enhance our AI-native investment analysis platform. We offer competitive salaries, a commitment to professional growth, and a supportive environment where your contributions are valued and recognised.
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! Be ready to explain how your skills align with their needs. Use examples from your past experiences to demonstrate your technical fluency and problem-solving abilities.
✨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 are proactive!
We think you need these skills to ace Product Manager, Data, London
Some tips for your application 🫡
Show Your Technical Fluency:Make sure to highlight your technical skills in your application. We want to see that you can read schemas and understand APIs, so don’t shy away from mentioning any relevant experience or projects that showcase this.
Be Clear and Concise:When writing your application, clarity is key! Use straightforward language and get straight to the point. We appreciate crisp specs and clear communication, so make sure your application reflects that.
Demonstrate Your Partnership Skills:Since this role involves working closely with partners, share examples of how you've successfully collaborated with external teams in the past. Show us that you can lead discussions and drive decisions effectively.
Use AI Tools to Your Advantage:If you’ve used AI tools like Cursor or Claude, mention it! We love seeing candidates who are comfortable with technology and can leverage these tools to enhance their work. It shows you're already aligned with our team's approach.
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 understanding of the financial data landscape. Brush up on key concepts like schemas, APIs, and data pipelines. Be prepared 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 demonstrate your technical skills during the interview. Talk about your experience writing specs and how you've collaborated with engineering teams in the past. Use examples that highlight 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 of examples where you've successfully led discussions or sessions with other teams. Be ready to explain 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 can be complex and unpredictable. Share experiences where you've tackled ambiguous situations or chased down edge cases. Highlight your QA mindset and how you proactively identify potential issues before they arise, showcasing your analytical skills and attention to detail.
