Lead Applied AI and Data Scientist | IFS Copperleaf

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 Copperleafs 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 Copperleafs 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 roadmaps 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 todays decisions become tomorrows 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.

Qualifications :

You Have the Following Background:

This is a founding Champion-level Lead Applied AI and Data Scientist role. We are looking for:

• An advanced degree (MS or PhD) in a quantitative field such as Computer Science Statistics Mathematics Physics or Engineering or equivalent hands-on depth.
• Deep hands-on applied data science and ML experience spanning both classical ML (forecasting anomaly detection classification clustering and causal or statistical modeling) and modern generative AI and LLM work.
• A proven track record in experimentation and the rigorous evaluation of ML LLM and agent systems (LLM-as-judge programmatic evaluators statistically sound thresholds and human-in-the-loop feedback).
• Hands-on experience with fine-tuning and distillation RAG and grounding prompt optimization embeddings and semantic search and agentic frameworks and orchestration.
• Production ML ownership: MLOps model versioning drift monitoring retraining and deployment.
• The ability to build golden datasets and evaluation corpora from disparate imperfect source data.
• Advanced proficiency in Python and SQL with fluency in ML libraries and with both major LLM APIs and open-weight models.
• Cloud experience with Azure preferred (Azure AI Foundry); Databricks a plus.
• Excellent communication and technical leadership skills with a strong instinct for interpretability and the ability to explain model and LLM behavior to technical and non-technical audiences alike.
• Comfort leading through ambiguity in a fast-moving space paired with a pragmatic high-quality delivery standard.

Nice to Have:

• Experience standing up a data science ML or applied-AI function guild or practice and mentoring others.
• Domain experience in regulated asset-intensive industries (utilities energy mining or oil & gas).
• Causal inference and experience with benchmarking peer-index or network-effect data products.
• Governance- and compliance-aware AI (auditability grounding entitlements EU AI Act).

Additional Information :

What Were Offering

• Salary Range: $113000 to $152000 CAD annually variable bonus
• Flexible paid time off including sick and holiday
• Medical dental & vision insurance
• RRSP matching
• Life insurance and disability benefits
• Community involvement and volunteering events

Use of Artificial Intelligence in Recruitment
As part of our recruitment process we may use automated tools including artificial intelligence to help screen and assess applications based on jobrelated criteria such as skills experience and qualifications.
These tools do not make hiring decisions. All employment decisions are reviewed and made by members of our hiring team.

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles while also valuing inclusive workplace experiences. By fostering a sense of community we drive innovation strengthen connections and nurture belonging. Our commitment ensures you can work in a way that suits you best while also engaging with colleagues to share ideas and build meaningful relationships.

Remote Work :

No

Employment Type :

Full-time