Senior Data Scientist

Job Description

RBC Technology Infrastructure seeks a full stack Data Scientist (DS) to explore and operationalize big data sources to reduce outage and down time for RBC services, improving user experience and saving costs. The role requires applied research and problem solving expertise, with experience developing and deploying production grade AI/ML solutions, and broad knowledge of statistics, analytics, ML and strong programming skills.

RBC Technology Infrastructure seeks a full stack Data Scientist (DS) to explore and operationalize big data sources to reduce outage and down time for RBC services that leads to improve user experience and save costs. Seeking a DS with experience in applied research and problem solving to join our team.

• Lead full life-cycle Data Science solutions from beginning to model deployment and monitoring and partner with the engineering team to ensure best practices for ML model deployment.
• Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.
• Work with (Python, Apache Spark, PySpark, R, Scala, SQL, No
  
  SQL, etc.) to obtain, integrate, manipulate, and analyze data from multiple sources.
• Expertise in 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 visualize targeted big data sources.
• Lead data exploration and analytic projects and provide ongoing coaching of big data topics (visualization, data mining, analytic techniques).
• Explore and implement semantic data capabilities through NLP, text mining and machine learning techniques.
• Oversee the 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.

• 5+ years of industry experience on real‑world problems. University, Master or Ph.D. degree in an analytical field of study (e.g., Computer Science, Engineering, Mathematics, Statistics, or related quantitative field).
• Experience with AI/ML infrastructure and model deployment for Gen AI applications in production environments and supporting enterprise‑scale use cases.
• Strong foundation in ML and AI basics, knowledge of inferencing, fine‑tuning, model architectures, embeddings, and hands‑on experience with modern frameworks such as PyTorch, Tensor Flow, Scikit‑Learn, or Hugging Face Transformers.
• Hands‑on experience designing graph data models and working with graph databases (Neo4j, Amazon Neptune, Tiger Graph) or knowledge graph frameworks (RDF/OWL, property graphs, SPARQL).
• Familiar with software engineering industry best practices, including coding standards, testing methods, code reviews, and version control.
• Experience working with technical and non‑technical project stakeholders to scope, formulate, deploy, and maintain data science systems.
• Self‑driven problem solver, able to adapt and thrive in a dynamic, ambiguous, customer‑faced environment.
• Familiarity with GIT (Git Hub).
• Strong communication, collaboration, and problem‑solving skills.
• Ability to prioritize work and manage multiple work streams concurrently.
• In‑depth knowledge of machine learning and deep learning algorithms.
• Excellent working with structured and non‑structured data. Proficiency in Python, PySpark, SQL.
• Experience with cloud‑based data platforms such as Azure or AWS. Experience with data visualization tools such as Tableau, Looker, and Power BI.

• Experience architecting large‑scale ML systems.
• Experience with reinforcement learning (DynaQ/Q+, SARSA, TD, Monte Carlo).
• Experience with GenAI LLM models.
• Experience with MLOps workflow.
• Knowledge in AIOps domain.
• Knowledge of IT operation monitoring tools (Dynatrace, Moog, GEM, Pager Duty, etc.).

• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable.
• Leaders who support your development through coaching and managing opportunities.
• Ability to make a difference and lasting impact.
• Work in a dynamic, collaborative, progressive, and high‑performing team.
• A world‑class training program in financial services.
• Opportunities to do challenging work and take on progressively greater accountabilities.
• Opportunities to build close relationships with clients.
• Access to a variety of job opportunities across business and geographies.