Sr. Quantitative Engineer

Senior Quantitative Engineer
Location: Karachi, Pakistan
Employment Type: Full-time, on-site
About Lumida
Lumida is an AI-native modern wealth advisor and Wealth Architect for owners and creators.
Our mission is to make money intelligent. We bring investing, trust and estate planning, and tax strategy together in a single wealth architecture—helping clients grow, protect, and transfer wealth with greater intention.
Our investment platform spans public markets and differentiated alternatives, including select pre-IPO opportunities for qualified clients. Our approach is institutional in its breadth, non-consensus in its thinking, and deeply personal in its application.
Lumida is a tech-first, AI-native firm built by a technology founder. We are making significant investments in proprietary AI Services that identify opportunities across a client’s financial life, strengthen investment research, automate client-service workflows, and help our advisors deliver faster, smarter, and more personalized advice.
Our team shares the mindset of our clients: curious, original, and driven to build what does not yet exist. Lumida is venture-backed, with real traction, an ambitious product vision, and a rapidly growing community.
The Role
Lumida is seeking a Senior Quantitative Engineer to help build the quantitative research and production systems behind our investment platform.
You will work closely with the CEO/CIO, quantitative researchers, and engineers to develop systematic investment strategies, portfolio-construction tools, factor and risk models, and reliable quantitative infrastructure.
The role spans Lumida Invest, our internal CIO platform, and the quantitative capabilities used by our investment team and advisors. It requires strength in both quantitative research and production-quality Python engineering.
What You’ll Own
Backtesting and Data Integrity
Refine and extend Lumida’s backtesting engine, research libraries, and model-validation framework.
Build reliable data pipelines for market, fundamental, alternative, and internal datasets.
Maintain accurate total-return and price-return series by correctly processing dividends, ordinary and special distributions, stock splits, reverse splits, spinoffs, symbol changes, mergers, delistings, and other corporate actions.
Validate adjusted and unadjusted price data and identify inconsistencies across vendors and data sources.
Prevent survivorship bias, look-ahead bias, timestamp leakage, stale observations, and other common sources of invalid backtests.
Create comprehensive unit, integration, regression, and data-quality tests for research and production systems.
Establish reproducible research environments, versioned datasets, documented assumptions, and reliable model releases.
Monitor production data and models for missing data, unexpected changes, signal failures, and performance deviations.
Quantitative Research and Strategy Development
Research, test, and refine systematic strategies across U.S. and global equities, commodities, digital assets, and other liquid markets.
Analyze factor signals, technical indicators, cross-sectional relationships, time-series behavior, market regimes, and statistical hypotheses.
Develop models for alpha generation, security selection, asset allocation, position sizing, portfolio optimization, hedging, and risk forecasting.
Evaluate statistical significance, economic rationale, robustness, factor decay, turnover, capacity, transaction costs, and execution constraints.
Apply out-of-sample testing, walk-forward analysis, sensitivity testing, and other rigorous validation methods.
Work with the CIO team to translate investment theses into systematic rules, research programs, and implementable strategies.
Portfolio Construction, Tax-Aware Strategies, and Risk
Build portfolio-optimization frameworks for long-only, long/short, beta-aware, factor-aware, and multi-asset strategies.
Develop tax-loss-harvesting and security-substitution strategies that account for wash-sale restrictions, holding periods, tax lots, realized gains and losses, transaction costs, portfolio drift, and tracking error.
Implement wash-sale-aware logic across accounts and substantially identical securities where applicable.
Build substitution models that identify suitable replacement securities while preserving intended factor, sector, industry, risk, and portfolio exposures.
Model portfolio exposures across market beta, sectors, styles, factors, liquidity, concentration, volatility, correlation, and drawdown risk.
Develop controls covering position size, turnover, leverage, liquidity, trading costs, and implementation risk.
Build capabilities for scenario analysis, stress testing, performance attribution, and portfolio diagnostics.
Production Quantitative Engineering
Design and maintain scalable Python systems for data ingestion, backtesting, portfolio construction, signal generation, optimization, and model monitoring.
Convert validated research into modular, tested, documented, and production-ready services.
Build APIs, scheduled workflows, alerts, and controls that make quantitative models reliable and accessible across Lumida’s products.
Establish strong engineering practices across Git, code review, automated testing, release management, documentation, and production monitoring.
Support the progression of strategies from research through validation, approval, deployment, and ongoing monitoring.
Lumida Invest and Internal CIO Tools
Develop quantitative capabilities supporting the strategies delivered through Lumida Invest.
Build internal and client-facing tools for portfolio optimization, tax-loss harvesting, security substitution, factor exposure, risk analysis, strategy comparison, and investment decision support.
Translate quantitative outputs into clear, useful experiences for investment professionals, advisors, and app users.
Partner with product and engineering teams to integrate models into internal and client-facing applications.
AI-Native Development
Use modern AI coding tools to accelerate research, software development, testing, documentation, and debugging.
Apply machine learning where it improves forecasting, classification, portfolio construction, risk management, or research efficiency.
Evaluate models using appropriate baselines, validation methods, explainability, and monitoring.
Build AI-enabled research tools and internal applications that expand the investment team’s analytical capabilities.
Research Communication and Leadership
Produce clear research notes, model specifications, validation reports, and technical documentation.
Explain methodology, assumptions, limitations, and results to technical and investment audiences.
Work closely with the CEO/CIO to prioritize research and engineering initiatives with the greatest investment and product impact.
Raise the technical standard of the quantitative function through ownership, code review, documentation, and mentorship.
Candidate Profile
The ideal candidate has:
Five or more years of experience in quantitative engineering, quantitative research, systematic investing, or a closely related field.
Advanced proficiency in Python, including Pandas, NumPy, SciPy, scikit-learn, statsmodels, and relevant optimization and quantitative libraries.
Strong SQL skills and experience building reliable financial data pipelines and analytical datasets.
Hands-on experience designing or materially improving a backtesting system.
Strong knowledge of market-data conventions, adjusted and unadjusted prices, total-return calculations, and corporate-action processing.
Direct experience implementing tax-aware portfolio strategies, including wash-sale-aware tax-loss harvesting or security-substitution logic.
Strong grounding in probability, statistics, econometrics, linear algebra, optimization, and time-series analysis.
Practical knowledge of factor investing, portfolio theory, risk modeling, backtesting, and empirical finance.
Experience taking quantitative strategies or models through validation, implementation, and production monitoring.
Strong software-engineering practices, including modular design, automated testing, Git, code review, documentation, and release management.
Experience using AI-assisted development tools to improve the speed and quality of research and engineering work.
Strong written and verbal communication skills.
A record of excellence, leadership, ownership, and measurable impact.
The initiative to define ambiguous problems, develop a rigorous approach, and carry the work through implementation.

Employment type
Full-Time

Work arrangement
No