Senior Data Scientist

What is the opportunity? RBC Technology Infrastructure is seeking a full‑stack Data Scientist to explore and operationalize big data sources to reduce outages and downtime for RBC services, thereby improving user experience and saving costs. The successful candidate will have experience developing and deploying production‑grade AI/ML solutions, with broad expertise in statistics, analytics, machine learning, and strong programming skills.

What will you do?

Lead full life‑cycle data science solutions from model development to deployment and monitoring, partnering with engineering to ensure best practices for ML deployment.

Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and deliver valuable discoveries that drive prototype development and product improvement.

Use tools such as Python, Apache Spark, PySpark, R, Scala, SQL, NoSQL, and related technologies to obtain, integrate, manipulate, and analyze data from multiple sources.

Perform statistical data analysis (e.g., univariate/bivariate analysis) and data quality assessment.

Build machine learning, deep learning, and statistical models to solve specific business problems.

Develop predictive data models, anomaly detection models, quantitative analyses, and visualizations of targeted big data sources.

Lead data exploration and analytic projects and coach on big data topics such as visualization, data mining, and analytic techniques.

Explore and implement semantic data capabilities through NLP, text mining, and machine learning techniques.

Oversee acquisitions and ingestions of data from structured and unstructured sources, ensuring quality and comprehensiveness.

Utilize APIs to collect data from various products into the data warehouse database.

What do you need to succeed? Must have:

5+ years of industry experience on real‑world problems, and a quantitative degree (e.g., BSc, MSc, PhD in Computer Science, Engineering, Mathematics, Statistics, or related field).

Experience with AI/ML infrastructure and model deployment for Gen AI applications in production environments and enterprise‑scale use cases.

Strong foundation in ML and AI basics, including inference, fine‑tuning, model architectures, and embeddings; hands‑on experience with PyTorch, TensorFlow, Scikit‑Learn, or Hugging Face Transformers.

Hands‑on experience designing graph data models and working with graph databases (Neo4j, Amazon Neptune, TigerGraph) or knowledge‑graph frameworks (RDF/OWL, property graphs, SPARQL).

Familiarity with software engineering best practices such as coding standards, testing, code reviews, and version control.

Experience working with technical and non‑technical stakeholders to scope, formulate, deploy, and maintain data science systems.

Self‑driven problem‑solver able to adapt in a dynamic, ambiguous, customer‑facing environment.

Experience with GIT (GitHub).

Strong communication, collaboration, and problem‑solving skills.

Ability to prioritize work and manage multiple streams concurrently.

In‑depth knowledge of machine learning and deep learning algorithms.

Excellent experience with Python, PySpark, SQL.

Experience with cloud‑based data platforms such as Azure or AWS, and data visualization tools such as Tableau, Looker, and Power BI.

Nice‑to‑have

Experience architecting large‑scale ML systems.

Knowledge of reinforcement learning techniques (e.g., DynaQ, SARSA, TD, Monte Carlo).

Experience with GenAI LLM models.

Experience with MLOps workflows.

Knowledge in AIOps domain.

Knowledge of IT operation monitoring tools (e.g., Dynatrace, Moog, GEM, PagerDuty).

What’s in it for you?

A comprehensive Total Rewards Program including bonuses, flexible benefits, competitive compensation, commissions, and stock where applicable.

Leadership support through coaching and developmental opportunities.

Opportunity to make a lasting impact and reach your potential.

Work in a dynamic, collaborative, high‑performing team.

A world‑class training program in financial services.

Opportunities for challenging work, progressive accountability, and building client relationships.

Access to a variety of job opportunities across business lines and geographies.

Job Skills Actuarial Modeling, Big Data Management, Commercial Acumen, Data Mining, Data Science, Decision Making, Machine Learning, Natural Language Processing, Predictive Analytics, Python.

Additional Job Details

Address: RBC CENTRE, 155 Wellington St W, Toronto

City: Toronto

Country: Canada

Work hours/week: 37.5

Employment Type: Full time

Platform: TECHNOLOGY AND OPERATIONS

Job Type: Regular

Pay Type: Salaried

Posted Date: 2026‑01‑27

Application Deadline: 2026‑05‑10 (applications accepted until 11:59 PM on the day prior to the deadline)

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