Lead Applied AI and Data Scientist | Copperleaf Job Vancouver area,BC Canada,IT/Tech

Position: Lead Applied AI and Data Scientist | IFS Copperleaf
Job Description

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…