Quantitative Research Intern at Astera Holdings

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Location: Remote (U.S. Preferred)
Type: Full-Time
Reports to: President

About Astera Holdings

Astera Holdings is building the operating system for real-world events — a unified intelligence layer that structures chaotic information, models uncertainty, and makes the future computational.

This system, Astera

OS, powers our ecosystem end-to-end:

Astera AI B2C: The first consumer‑grade OS for event investing.

Astera Capital: Systematic trading across sports, economics, politics, and global prediction markets.

Astera Analytics: Enterprise‑grade event intelligence and forecasting systems.

Our culture is a Meritocracy of Minds — scientific rigor, radical intellectual honesty, high‑agency execution, and a relentless focus on truth and results.

This is a foundational Quant Research role within Astera’s trading and forecasting engine. As a QR, you will design, test, and deploy quantitative models that directly drive live trading and the Reflexivity Engine — our real‑time uncertainty modeling core.

This is not a theoretical research job.
This is production quant R&D, where models leave notebooks and enter live execution.

You’ll work across modeling, time‑series forecasting, feature engineering, simulation, and signal extraction, all unified under Astera’s thesis of turning global uncertainty into a computational, tradable domain.

• Develop and validate predictive models spanning time‑series, ML forecasting, Bayesian inference, and event‑driven signal extraction.
• Turn prototypes into production systems by working tightly with engineering and infra.
• Build factor libraries, feature pipelines, and signal engines grounded in statistical rigor.
• Backtest trading strategies with clean methodology, robust simulation, and risk‑adjusted performance frameworks.
• Collaborate directly with leadership on research direction, hypothesis formulation, and model architecture.
• Contribute to the Reflexivity Engine, improving its reasoning, uncertainty modeling, and structural forecasting capabilities.
• Drive PnL and model performance through high‑quality research and rapid iteration.

We don’t care about degrees or pedigree.
We care about ability, rigor, and grit.

• 1. Quantitative Modeling
  • Time‑series forecasting
  • Probabilistic models
  • ML regression/classification
  • Bayesian approaches
• 2. Machine Learning for Signals
  • Ensemble models
  • Deep learning for structured/unstructured event data
  • Evaluation, calibration, uncertainty quantification
  • Simulation frameworks
  • Performance attribution
  • Live signal monitoring & stability analysis
• (Not required, but a strong edge)
• Python mastery
• Building reliable backtesting or research frameworks

• GRIT
• First‑principles thinker
• Obsession with truth and empirical rigor
• High‑agency, low‑ego
• Comfortable operating in ambiguity and speed

Your research hits production. You will see your models deployed into real strategies, not theoretical papers.

Small team = massive surface area. You’ll shape research direction, architecture, and model design.

True multidisciplinary exposure: quant, ML, systems, forecasting, and event‑driven modeling.

Direct collaboration with founders and senior engineers.

Send your resume and any relevant links (Git Hub, research, portfolio) to:
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