The Opportunity

We are looking for a Lead Applied AI and Data Scientist to establish and lead the applied AI & data science practice, a new capability within IFS Copperleaf’s AI Engineering & Transformation pillar. This is the applied-science engine beneath our AI products: the discipline of models, data, evaluation, and tuning that makes every AI capability work, and it is largely yours to build.

IFS Copperleaf’s mission is to make every capital decision explainable, defensible, and agentic, moving our platform from a system of record to a system of action. The Lead Applied AI and Data Scientist will build and advance the models and machine-learning methods, data pipelines, evaluation and tuning frameworks, and monitoring that power that mission across our AI products today and the broader roadmap ahead, including richer agentic capabilities and orchestration.

Reporting to the Senior Director, AI Engineering & Transformation, you will be the Champion for the cell and its technical and operational lead. Champions own the technical direction, standards, and craft of their cell; hiring, performance, and compensation accountability sits with the Senior Director. You will partner closely with development teams, lead through influence and hands-on technical work, and demonstrate what a best-in-class AI engineering practice looks like.

This is a deliberately greenfield leadership role. You will define the methods, tooling, standards, and technical direction as the space develops, while remaining deeply hands-on. Expect to move fluidly between building models yourself, guiding complex technical decisions, and building the discipline and capabilities around them.

About Applied AI & Data Science

The cell spans two complementary halves. The first is classical applied data science, including forecasting, anomaly detection, classification, clustering and similarity, correlation and causal analysis, and risk and signal modeling. The second is modern AI modeling and tuning including evaluating, fine-tuning, grounding, and optimizing large language models and agentic systems so our products behave predictably, defensibly, and at scale.

Both halves sit on the AI Foundation. Today that foundation includes a GenAI agent framework, a tool system, vector stores, streaming and telemetry, and a separate ML service; the near-term build ahead includes a domain-model training pipeline, agentic safety and governance, cost-aware model routing, and domain-specific semantic search. The Lead Applied AI and Data Scientist is central to advancing all of it.

Key Responsibilities

Structure & Stand Up the Discipline

• Define and lead how applied data science and AI modeling are practiced across the pillar, including the methods, tooling, standards, and reusable pipelines for a nascent, greenfield capability you will shape and grow.
• Own these shared practices through the AI Center of Excellence (evaluations, patterns, governance, and craft), and serve as the central applied-science partner to the product cells and the AI Foundation.
• Provide technical leadership and mentorship to engineers and scientists, help hire toward a best-in-class team, and raise the modeling and evaluation bar across the pillar.

Data Foundations for Modeling

• Build golden and curated datasets from disparate, imperfect sources to support model development, benchmarking, and testing.
• Help stand up a domain model training pipeline that trains planning-specific models on appropriately governed, anonymized data for industry benchmarks.

Applied Data Science & Modeling

• Design, build, and productionize analytical and predictive models across the toolkit: forecasting, anomaly detection, classification, clustering and similarity, correlation and cause-and-effect (causal) analysis, and risk and signal modeling, with a strong bar for interpretability and for explaining the drivers behind results.
• Apply these methods to the roadmap’s decisioning problems as they mature, for example, trend and signal risk profiled against an investment portfolio, precedent and case-similarity detection, peer benchmarking and indices, asset-performance and remaining-life modeling, and scenario optimization.

Generative & Agentic AI — Modeling & Tuning

• Explore, evaluate, and adopt the newest LLMs and agentic frameworks; guide retrieval-augmented generation (RAG) and grounding, citation integrity, prompt and model-routing optimization, fine-tuning and distillation, and domain-specific embeddings and semantic search.
• Guide product-engineering teams on structuring data for LLM and agent consumption and on building reliable retrieval and evaluation pipelines.

Evaluation, Verification & Quality

• Build rigorous frameworks to quantitatively evaluate model, LLM, and agent performance: accuracy, repeatability, hallucination rate, and grounding and citation consistency, using LLM-as-judge, programmatic evaluators, and classical statistical methods.
• Ensure AI-driven features clear statistically sound thresholds so they surface signal rather than noise, and integrate human-in-the-loop feedback.
• Contribute to agentic safety and governance, including guardrails, hallucination detection, and compliance checking, for regulated, auditable AI.

Production, MLOps & Monitoring

• Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.
• Implement drift detection, retraining strategies, model versioning, and production monitoring.
• Partner with the Agentic Platform pillar on scalable serving, cost and usage telemetry, and cost-aware model routing.

Stay Ahead

• Track the AI frontier and self-disrupt deliberately, bringing new models, tools, and methods into the practice before the roadmap demands them.
• Keep modeling and evaluation aligned with the “explainable, defensible, agentic” north star and with AI governance and compliance expectations (EU AI Act and sector frameworks).

Building Knowledge

• Develop deep working knowledge of the IFS Copperleaf Suite and the AI products and the decisioning problems they solve.
• Build domain fluency in asset-intensive, regulated capital planning, including value frameworks, risk and asset data, and the regulatory context that shapes products like Regulatory Intelligence.
• Stay fluent in Agentic Operating Model (AOM) practices, AI tools, and the research frontier, applying them to raise modeling and evaluation quality.

Applying Skills

• Bring both the modeling rigor of a data scientist and the production instincts of an ML engineer, from statistical soundness through deployment, monitoring, and drift.
• Translate ambiguous, evolving product needs into well-structured modeling and data problems.
• Communicate model and LLM behavior, tradeoffs, and interpretability clearly to both technical and non-technical audiences.
• Design feedback loops so that today’s decisions become tomorrow’s training signal.

Influencing Behavior

• Set and uphold the technical standard for a best-in-class AI engineering practice: go deep, ship quality, and prove it with evidence and verification.
• Stay ahead of the curve and self-disrupt; invest in the capabilities the future will need before they are demanded.
• Master and advance the Agentic Operating Model, establish shared practices through the AI Center of Excellence, mentor others, and raise the performance of the people and teams around you.