Lead Quantitative Researcher

Who we are

New Frontier is an investment technology firm known for converting rigorous research into practical portfolio solutions for advisors and institutions. Our patented Michaud Optimization process and Intelligent Rebalancing power market-adaptive, risk-managed ETF portfolios, supported by continuous research, robust analytics, and disciplined implementation.

The opportunity

We're adding a Lead Quantitative Researcher
to help advance the intellectual foundations and day-to-day implementation of our investment process. You'll design research, code it, test it, publish it – and see it in production portfolios used by sophisticated clients.

What you'll do

Applied research & portfolio innovation

• Develop research that strengthens portfolio construction, risk modeling, and asset allocation – especially under estimation uncertainty.
• Turn theory into practice: prototype and validate models on real market and ETF data.
• Advance multi-asset investing (equity, fixed income, alternative assets) with attention to long‑horizon, tax-aware, and retirement contexts.
• Contribute to capital‑market assumptions frameworks and robust estimation methods.

Optimization & analytics engineering

• Build and maintain research code, libraries, and experiment pipelines (Python, R, or MATLAB), emphasizing reproducibility, unit testing, and peer-review.
• Improve algorithms for optimization, statistical estimation, and signal stability.
• Partner with data engineering on dataset curation.

Investment process & governance

• Participate in Investment Committee discussions, frame trade-offs clearly with evidence, risk metrics, and client outcomes in mind.
• Translate research into portfolio guidelines, monitoring rules, and rebalancing/implementation.

Thought leadership & communication

• Co-author peer-reviewed and white papers, research notes, and practitioner pieces; present internally and at external forums.

What you bring

• PhD
  (or ABD close to completion) in Finance, Economics, Financial Engineering, Applied Math, Statistics, or related field; exceptional
  master's
  candidates with strong publication or industry impact considered.
• 5+ years
  of applied research or investment industry experience.
• Programming:
  Proficient in
  Python,
  R, or MATLAB
• Quant toolkit:
  Portfolio theory and optimization, time‑series/statistical modeling, robust/Bayesian estimation, simulation/bootstrapping/Monte Carlo, and risk modeling.
• Domain fluency:
  Multi-asset and ETF analytics; after-tax and constraints-aware portfolio construction; transaction‑cost and rebalancing frameworks.
• Communication:
  Clear writer and presenter; able to explain complex quantitative ideas to both academics and practitioners.
• Mindset:
  Team-oriented, intellectually curious, pragmatic, and fiduciary-minded; you enjoy owning problems end‑to‑end.

Nice to have

• Experience with household/goal-based optimization, retirement glide paths, liability-aware or draw-down-aware design.
• Exposure to factor/risk‑model integration, scenario design, and regime‑switching or macro-linked estimation.
• Familiarity with productionized research workflows.
• Publication record (peer-reviewed journals) or evidence of impactful practitioner research.

Why join New Frontier

• Work at the frontier:
  Contribute to a patented optimization and rebalancing platform used in live portfolios for sophisticated clients.
• Impact and visibility:
  Small, senior team where your research can ship quickly and be read by advisors, CIOs, and the broader investment community.
• Research culture:
  Collegial, rigorous, and collaborative—publish, present, and keep learning.
• Growth:
  Direct exposure to the Investment Committee and leadership; opportunities to mentor and to lead initiatives.
• Rewards:
  Competitive compensation (base + performance bonus) and comprehensive benefits; professional development support.

What success looks like (first 12 months)

• Ship at least one research enhancement into production portfolios (e.g., estimation, optimization, or rebalancing improvement).
• Co-author several client-facing research notes or white papers: present findings to the Investment Committee and advisor audiences.
• Publish a peer-reviewed paper.
• Raise the bar on research engineering standards (testing, reproducibility, documentation).