Sr. Data Scientist

RBC•New York, NY

1d•$85,000 - $145,000•Onsite

About The Position

The Alternative Data & AI team works with clients and leverages alternative data (structured and unstructured non-financial data) datasets and applying advanced AI techniques to develop financially relevant factors, actionable insights, and differentiated content for Capital Markets clients. The Sr. 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.

Requirements

Master's or PhD in Mathematics, Statistics, Computer Science, or another quantitative field.
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.
Demonstrated ability to perform complex data analysis on large volumes of structured and unstructured data, and to present findings clearly to non-technical stakeholders.
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.
Experience in index construction and factor model development, including the design, backtesting, and ongoing maintenance of quantitative indices derived from alternative or financial data.
Creative and rigorous approaches to using alternative datasets to generate insights into the financial performance of companies and macroeconomic trends.
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.
Expert technical documentation skills.

Nice To Haves

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.
Good understanding of financial markets, equity research workflows, and quantitative investing.
Familiarity with index governance, rebalancing methodologies, and index licensing frameworks is a plus.
GitHub repository demonstrating applied data science or research projects is appreciated.

Responsibilities

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.
Identify and evaluate novel data sources, to develop unique and proprietary insights for institutional research teams and clients — including web scraping, geolocation data, satellite imagery, NLP on unstructured data sources (such as news), and other non-traditional signals.
Collaborate closely with equity and macro research teams, technology teams, and cross-functional stakeholders on strategic initiatives, providing expertise in advanced analytics, data modelling, data cleansing, and data optimization.
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.
Champion emerging technology trends and tools that can be leveraged to further the Alternative Data & AI platform, staying current with developments in generative AI, large language models, and alternative data sourcing.
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.
Proactively identify new opportunities for engaging Research teams with novel data products and AI-driven analytical frameworks.
Mentor and develop junior data scientists, providing technical guidance during project execution and fostering a culture of continuous learning within the team.
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.
Proactively identify operational risks and control deficiencies in the business.
Review and comply with Firm Policies applicable to your business activities.
Escalate operational risk loss events, control deficiencies, and risks to your line manager and the relevant risk and control functions on a timely basis.