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About This Role
Job Description
### What is the opportunity?
### 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.
### What will you do?
- ### 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.
### Front Office
- ### 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.
### What do you need to succeed?
### Must\-have
- ### 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\-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.
- ### 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.
### What’s in it for you?
### We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.
- ### A comprehensive Total Rewards Program include competitive compensation and flexible benefits, such as 401(k) program with company\-matching contributions, health, dental, vision, life, disability insurance, and paid\-time off.
- ### 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.
- ### Opportunities to do challenging work.
- ### Opportunities to build close relationships with clients.
### The expected salary range for this particular position is $85,000\-$145,000 (New York), depending on your experience, skills, and registration status, market conditions and business needs.
### You have the potential to earn more through RBC’s discretionary variable compensation program which gives you an opportunity to increase your total compensation, provided the business meets its performance targets and you meet your individual goals.
### RBC’s compensation philosophy and principles recognize the importance of a highly qualified global workforce and plays a critical role in attracting, engaging and retaining talent that:
- ### Drives RBC’s high\-performance culture
- ### Enables collective achievement of our strategic goals
- ### Generates sustainable shareholder returns and above market shareholder value
Job Skills
Actuarial Modeling, Big Data Management, Commercial Acumen, Data Mining, Data Science, Decision Making, Machine Learning (ML), Natural Language Processing (NLP), Predictive Analytics, Python (Programming Language)Additional Job Details
Address:
BROOKFIELD PLACE FKA 3 WORLD FINANCIAL CENTER, 200 VESEY STREET:NEW YORKCity:
New YorkCountry:
United States of AmericaWork hours/week:
40Employment Type:
Full timePlatform:
CAPITAL MARKETSJob Type:
RegularPay Type:
SalariedPosted Date:
2026\-07\-14Application Deadline:
2026\-09\-05Note: *Applications will be accepted until 11:59 PM on the day prior to the application deadline date above*
Our Employment Opportunities
At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. 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. 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.
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RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.
Salary Context
This $85K-$145K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At RBC, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($115K) sits 40% below the category median. Disclosed range: $85K to $145K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
RBC AI Hiring
RBC has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Minneapolis, MN, US, New York, NY, US, Seattle, WA, US. Compensation range: $145K - $230K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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