Data Scientist - AI and ML

Full Description

Data Scientist – Advanced (12-Month Contract)

Location: Downtown Toronto – Hybrid (4 days onsite per week)

Contract: 12 months, strong possibility of extension

Role Overview

We are seeking a highly skilled Data Scientist to design, build, and implement advanced machine learning and generative AI solutions that support risk, compliance, and operational efficiency initiatives.

This role combineshands-on data science, ML engineering, model deployment, and collaboration with non-technical business partners.

You will work with large, complex datasets, build end-to-end data pipelines, develop production-grade ML models, and create AI agents that enhance analytical capabilities across the organization.

What You’ll Do

• Develop and test AI agents and machine learning models that improve audit and operational workflows

• Collaborate with stakeholders to gather requirements and translate them into technical solutions

• Design and implement end-to-end pipelines: data ingestion, preprocessing, feature engineering, model development, validation, and deployment

• Apply supervised, unsupervised, and deep learning techniques (including NLP) to structured and unstructured data

• Build prototypes using modern frameworks, including LangGraph, LlamaIndex, and agentic workflows

• Research and evaluate new ML and GenAI methodologies; adapt them to production use cases

• Work with cross-functional teams (data, audit, engineering, PM/PO) to deliver scalable, enterprise-ready solutions

• Communicate complex findings clearly with both technical and non-technical audiences

Must-Have Qualifications

• 5–9 years of industry experience in data science or machine learning

• Graduate degree required – Master’s in Computer Science, Engineering, Mathematics, Statistics, or a thesis-based quantitative discipline

• Strong programming skills in Python, including experience building ML models and pipelines

• Experience with LangGraph, LlamaIndex, SQL, and modern ML workflows

• Solid understanding of ML methods: supervised/unsupervised learning, feature/representation learning, anomaly detection

• Experience developing high-quality, maintainable, production-ready code

• Familiarity with data preprocessing, statistical methods, and model evaluation

• Strong communication skills for collaborating with stakeholders, PMs/POs, and non-technical teams

Nice-to-Have Skills

• PhD in a quantitative or computational field

• Experience deploying models as REST APIs, dashboards, or batch processes

• Experience with containerization/orchestration (e.g., Docker, Kubernetes, OpenShift)

• Knowledge of deep learning architectures (CNNs, RNNs, Transformers, Autoencoders)

• Background in audit, risk, or compliance analytics

Technical Environment

Candidates will work daily with:

• Python (core language)

• LangGraph & LlamaIndex (agentic workflows & GenAI applications)

• SQL

• Docker

• Collaborative dev tools: Git, issue tracking, *nix environments