Royal Bank of Canada

Royal Bank of Canada

Sr. Data Scientist

New York City, New York · Senior · Full-time

Sponsorship not specified$85k-$145kDetected 7 hours ago
PythonExpressGitSQLNoSQLSnowflakeDatabricksMachine LearningTensorFlowscikit-learnPandasNumPyData AnalysisData EngineeringData ScienceData VisualizationNLPLLMsStatisticsResearchCollaborationMentoringPublic SpeakingActuarial Science

About the role

  • Job Description What is the opportunity?
  • Data Scientist on this team plays a key role in delivering AI and data-driven solutions to our Institutional Research stakeholders and clients, driving innovation at the intersection of alternative data and cutting-edge machine learning.
  • We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world.

Responsibilities

  • Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities.
  • RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.
  • Expand your limits and create a new future together at RBC.
  • Find out how we use our passion and drive to enhance the well-being of our clients and communities at jobs.rbc.com.
  • Build, maintain, and enhance data pipelines and infrastructure using Databricks, Snowflake, PySpark, and SQL to ensure scalable, reliable, and efficient data processing across large alternative datasets.
  • Coordinate, generate, and maintain alternative data products, presentations, models, and databases of unique, alternative, and proprietary insights that support client-facing research.
  • Drive the development of big data and alternative data capabilities, leading coordination of cross-functional engineering and research initiatives within the Alternative Data & AI team.
  • Design and develop proprietary indices and factor models, applying rigorous quantitative methodologies to construct, backtest, and maintain financially relevant indices derived from alternative data signals.
  • Demonstrated ability to perform complex data analysis on large volumes of structured and unstructured data, and to present findings clearly to non-technical stakeholders.
  • Experience in index construction and factor model development, including the design, backtesting, and ongoing maintenance of quantitative indices derived from alternative or financial data.

Requirements

  • Hands-on experience with Databricks for large-scale data processing and ML workflows, and Snowflake for cloud data warehousing and analytics.
  • Strong proficiency in PySpark for distributed data processing and SQL for data querying, transformation, and pipeline development across large datasets.
  • Deep expertise in data profiling, cleaning, feature engineering, and insight generation across diverse data types.
  • Expert working knowledge of Python and R, with strong overall coding abilities.
  • Expert-level experience with ETL processes across a variety of data types and formats.
  • Strong understanding of both NoSQL and SQL database architectures.
  • Provide senior-level research support to stakeholders as required, acting as a subject matter expert on alternative data methodologies, index construction, and AI-driven analytics.

Nice to have

  • Familiarity with data visualization tools and techniques such as D3, R, Qlik, Tableau, and/or Power BI.
  • Proficiency in standard Python libraries including pandas, NumPy, and Matplotlib.
  • Experience with ML Python libraries such as scikit-learn, TensorFlow, or PyTorch.
  • Experience with NLP Python libraries such as NLTK, spaCy, or Hugging Face - particularly for financial text analysis.
  • Exposure to generative AI and large language model (LLM) frameworks, with an interest in applying them to financial research use cases.
  • Prior experience in capital markets or institutional research environments.
  • Familiarity with index governance, rebalancing methodologies, and index licensing frameworks is a plus.
  • GitHub repository demonstrating applied data science or research projects is appreciated.

Compensation

  • $85k-$145k

Benefits

  • Lead the design and implementation of statistical, machine learning, and mathematical methodologies to solve complex research problems and perform advanced data analysis leveraging alternative datasets.
  • Mentor and develop junior data scientists, providing technical guidance during project execution and fostering a culture of continuous learning within the team.
  • 3+ years of experience in Data Science, Machine Learning, Natural Language Processing, or Statistics - ideally in a capital markets or financial research context.
  • Strong quantitative modelling skills, including statistical modelling, machine learning, and optimization techniques applied to financial or alternative datasets.

Company info

  • At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC.
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This listing is sourced directly from Royal Bank of Canada's careers page and normalized into a canonical job model.