You think in time series, signals, and regimes.
You care about insight quality, not academic purity.
You want your models tested by markets, not papers.
If you dislike messy data and real-world constraints, this is not your role.
You will build quantitative and ML-driven insight systems using structured time series data.
This role exists to turn raw financial data into actionable investor signals.
You will work closely with engineers to productionize quant logic.
- Develop models using structured financial time series
- Build insight generation and scenario analysis pipelines
- Collaborate with backend engineers to deploy models in production
- Evaluate signals based on real investor outcomes
- Improve attribution and explainability
After 3 months:
Shipping signals used internally.
After 6 months:
Signals used by customers.
After 12 months:
You shape how quant insights are built at Reflexivity.
- 5 plus years experience in quant, ML, or financial modeling
- Strong Python skills
- Startup experience on core systems
- Investment domain knowledge
- AI-assisted coding experience
- Prior buy-side or sell-side experience
- Experience with alternative data
- In-office team with high trust and high ownership
- Direct communication, minimal process, strong opinions backed by data
- Engineers are expected to think about product impact, not just code
- We move fast when it matters and slow down when correctness matters more
- Direct influence on how professional investors make decisions
- Hard problems at the edge of AI, data, and finance
- Real ownership and technical autonomy
- Senior peers who care about quality and outcomes
- Base salary: £110,000 to £200,000 depending on experience
- Equity included
- In-office role based in London
- No agency candidates
Top Skills
What We Do
We empower organizations and institutions with the capabilities to more effectively identify mental health challenges to ensure patients, team members, and constituents receive the care they need.
Toggle offers a new form of artificial intelligence that merges advances in computational cognition with machine learning. We call it Comp Cog AI, and it predicts anxiety, depression, and suicidal planning using a single 3-minute assessment involving images.
Its accuracy is cutting-edge against peer-reviewed findings for these three mental health challenges and is immune to manipulation or gaming, ensuring highly repeatable results.
Fast, accurate & inexpensive, Toggle’s solution is built on the latest developments in neuroscience and supported by deep academic review and competitive government funding.
Example Use Cases:
- Rapid & regular screening for Military, Defence and Public Safety personnel
- Screen for mental health during every visit to the Emergency Room or Medical Clinic
- Triage and best route callers in crisis line and telehealth environments
- Regularly assess at-risk groups remotely, triggering outreach where needed
- Offer screening to students and the workforce to deliver timely intervention
