Interviewee

Scott Hiemstra — Director Financial Strategist, CDW

Building the Infrastructure Behind Modern Quant Alpha

Quantitative investing is evolving rapidly. As data sources expand, execution speeds compress, and cross-asset complexity increases, the real competitive edge is no longer just in models — it’s in the infrastructure that powers them.

Today’s leading quant teams require AI-ready data platforms, low-latency connectivity, scalable compute, and governance frameworks that turn innovation into production-grade capability. The focus is shifting from isolated tools to integrated architecture.

Turning Alternative Data into Governed Signals

Alpha generation increasingly relies on alternative and unstructured data sources, including text, ESG feeds, and satellite imagery. The challenge is not just accessing these datasets, but ingesting, normalizing, and governing them at scale.

Modern AI-ready data platforms with GPU-accelerated pipelines allow quant teams to process large volumes of unstructured data efficiently. By integrating best-in-class NLP, LLM, ESG, and geospatial/vision tools and wiring them into feature stores, these datasets become reusable, governed components of the research stack rather than one-off experimental projects.

This approach ensures alternative data can be systematically leveraged, validated, and redeployed across strategies.

Eliminating Infrastructure Fragmentation

Much of the friction in systematic trading environments stems from fragmented infrastructure. Disconnected systems across front, mid, and back offices introduce latency, reconciliation risk, and operational inefficiencies.

Modernizing the underlying network and data topology addresses this directly. Low-latency connectivity, shared data models, and standardized schemas create a consistent foundation. Layering APIs, messaging frameworks, and observability across the stack ensures that trade events, risk metrics, and lifecycle states flow coherently from front to back office.

The result is reduced reconciliation risk and a unified, end-to-end workflow for quant and risk teams.

Real-Time AI for Execution Quality

Improving execution quality in real time requires low-latency, high-throughput environments. GPU- or accelerator-backed nodes positioned close to gateways and smart order routers allow adaptive routing, microstructure-aware models, and slippage-sensitive logic to infer at microsecond timescales.

These services are containerized, monitored, and governed like any other critical production system. Execution intelligence becomes scalable, observable, and reliable — not experimental.

Making Signal Pipelines Trustworthy

As models become more complex, data governance and MLOps become essential components of the signal pipeline.

Automated ingestion with SLAs, cataloging and lineage tracking across lakes and warehouses, and quality scoring attached to each dataset help ensure integrity at the data layer. Model registries and validation pipelines run diagnostics such as drift detection, leakage checks, and scenario testing before features or models are promoted to production workflows.

This integrated governance framework ensures signals are robust, auditable, and production-ready.

Supporting Cross-Asset Portfolio Construction

Quant teams increasingly manage both liquid and illiquid exposures. Supporting portfolio construction across public and private markets requires scalable compute, storage, and modeling environments.

A unified research and risk fabric enables cross-asset optimization, risk calibration, and scenario testing where macro, liquidity, and cash-flow views can be simulated together — rather than relying on separate toolchains for each asset class.

Preparing for Emerging Technologies

As quantum computing, reinforcement learning, and LLMs gain adoption in quant strategy development, practical implementation becomes the priority.

Near-term impact is centered on optimization problems, simulation acceleration, research automation, and code or strategy prototyping. Supporting this requires access to quantum-ready cloud platforms, RL-optimized compute environments, and enterprise-safe LLM stacks — all deployed within governed and secure architectures that prepare firms for longer-term innovation.

Designing for Modularity

Quant teams need flexibility, not all-or-nothing stacks.

Architectures built around APIs, containers, and infrastructure as code expose data ingestion, feature engineering, model training, and execution as modular services — often deployed on Kubernetes or similar orchestration frameworks. This allows firms to plug in proprietary components or third-party engines without being locked into a single vendor’s ecosystem.

The platform layer enables innovation rather than constraining it.

Expanding Beyond Equities

Systematic strategies increasingly extend into fixed income, volatility, and structured derivatives.

Supporting these strategies requires low-latency market data capture, GPU/HPC compute clusters for curve and surface modeling, high-performance storage tuned for yield and volatility simulations, and integration patterns for complex instrument libraries and cross-asset risk engines.

The architecture must flex across asset classes without creating siloed systems for each strategy.

Stress Testing for Sudden Market Changes

Robust model validation requires large-scale backtesting and simulation environments.

GPU and CPU clusters — deployed on-premises and burstable into cloud — allow teams to run thousands of stress scenarios in parallel. With job schedulers and orchestration frameworks integrated into the model registry and data platform, firms can test against historical crisis windows, synthetic shocks, and intraday microstructure events before going live.

Stress testing becomes systematic and scalable.

Bridging Quant and Discretionary Workflows

There is growing interest from discretionary managers seeking quant research and signal augmentation without fully transforming into systematic operations.

Explainable AI dashboards, portfolio analytics platforms, and secure LLM interfaces can overlay internal research, holdings data, and factor libraries. This allows managers to interrogate signals, run what-if scenarios, and augment positions with quant insights while preserving their fundamental workflows.

Scaling Research and Production

Supporting scalable research and production workloads requires flexible architecture across on-premises, cloud, GPU-accelerated, and hybrid compute environments.

These architectures are engineered for performance, cost efficiency, resilience, and uptime — while meeting the strict low-latency requirements of trading systems, real-time analytics, and model inference pipelines.

Orchestration frameworks allow firms to elastically scale compute for backtesting, simulation, and real-time execution without compromising security, compliance, or operational continuity.