Adoption Analytics & Insights Manager

Appnovation Technologies•Toronto, ON

5d•Hybrid

About The Position

Join forces with Appnovation to deliver high-impact results for one of our key strategic partners - a global leader in their field. This engagement offers a unique vantage point into a world-class organization with exceptional opportunities for professional growth and long-term career advancement. This role requires high-level autonomy, where you will define your own direction within strategic goals, influence multiple teams, and navigate high complexity and ambiguity to deliver tangible business results. You will bridge the gap between technical innovation and real-world business challenges by applying advanced statistical and machine learning techniques to build predictive models and derive actionable insights.

Requirements

Bachelor's degree in Computer Science or Engineering.
8–12 years of relevant experience leading complex technical projects with multi-team impact.
Expert capability in designing end-to-end ML systems for complex problems (including imbalanced data and concept drift).
Expert capability in applying advanced statistical methods (mixed models, survival analysis, Bayesian approaches).
Deep understanding of causal inference techniques, experimentation design (A/B testing, MABs), and deep learning architectures for unstructured data (e.g., NLP, Computer Vision).
Expertise in Python (including libraries like Pandas and NumPy) or R.
Proficiency with cloud platforms and their data/ML services (e.g., AWS Sagemaker, GCP Vertex AI, Azure ML Services).
Experience with big data technologies (e.g., Spark, Hadoop).
Familiarity with MLOps tools (e.g., Kubeflow, MLflow, Docker).
Proven ability to immerse in operations to acquire deep domain expertise, thinking like an insider to translate seamlessly between business needs and engineering solutions.
Ability to build complete applications rapidly across any technology stack, selecting the right tools to balance technical debt with delivery speed.
Experience architecting scalable data strategies across diverse teams and complex enterprise landscapes.
Experience defining data governance policies.
Experience mastering data quality frameworks.
Experience building robust integration solutions for undocumented schemas and disparate systems.
Experience driving the adoption of modern data warehousing and lakehouse architectures.
Experience leading AI governance initiatives.
Experience creating evaluation standards.
Experience training teams on rigorous AI verification and risk management.
Proficiency in leading rapid delivery initiatives using a prototype-first approach to validate solutions quickly before scaling.
A track record of seeking out undefined problems.
A track record of embedding with users to discover latent needs.
A track record of turning ambiguity into clear problem statements.
Polymath Oriented: You bridge gaps between engineering, design, business, and science, rapidly immersing yourself in new domains to speak the language of the business.
Curious Explorer: You seek out ambiguity rather than avoiding it, driving team curiosity through challenging questions and creating an environment that encourages experimentation.
Outcome Owner: You drive an accountability culture focused on business impact rather than just deliverables, owning relationships and making trade-offs between custom solutions and generalizable work.
Systems Thinker: You map complex system interactions across technical and business domains, anticipating cascading effects and understanding how technology changes impact operations.
Precise Communicator: You translate seamlessly between technical and business language, ensuring requirements are clear enough to enable AI generation and facilitate productive discussions with stakeholders.
Problem Discoverer: You embed with users to discover latent needs, turning ambiguity into clear problem statements rather than waiting for defined tasks.

Responsibilities

Define your own direction within strategic goals.
Influence multiple teams.
Navigate high complexity and ambiguity to deliver tangible business results.
Bridge the gap between technical innovation and real-world business challenges by applying advanced statistical and machine learning techniques to build predictive models and derive actionable insights.
Design end-to-end ML systems for complex problems (including imbalanced data and concept drift).
Apply advanced statistical methods (mixed models, survival analysis, Bayesian approaches).
Demonstrate a deep understanding of causal inference techniques, experimentation design (A/B testing, MABs), and deep learning architectures for unstructured data (e.g., NLP, Computer Vision).
Build complete applications rapidly across any technology stack, selecting the right tools to balance technical debt with delivery speed.
Architect scalable data strategies across diverse teams and complex enterprise landscapes.
Define data governance policies.
Master data quality frameworks.
Build robust integration solutions for undocumented schemas and disparate systems.
Drive the adoption of modern data warehousing and lakehouse architectures.
Lead AI governance initiatives.
Create evaluation standards.
Train teams on rigorous AI verification and risk management.
Lead rapid delivery initiatives using a prototype-first approach to validate solutions quickly before scaling.
Seek out undefined problems.
Embed with users to discover latent needs.
Turn ambiguity into clear problem statements.