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About This Role
Job Description
What is the opportunity?
In this role as a Lead Data Scientist you will analyze, design and implement data science / machine learning solutions using RBC’s enterprise suite of analytics tools. USWM Applied AI group specializes in taking full advantage of large data sets to explore and discover new insights that would have not been possible with traditional analytics. Leveraging leading edge technologies and capabilities, the group applies machine learning and statistical modelling techniques to help RBC understand the changing business environment, discover new growth opportunities and determine where business improvements can be made.
This is a senior individual contributor role on a greenfield Applied AI squad. You will own the full data science lifecycle — from problem framing and exploratory analysis through model development, evaluation, and production performance. You'll work alongside AI engineers and MLOps to bring models and data\-driven features into real financial services workflows. This isn't a notebook\-and\-dashboard role: you write production Python, collaborate closely with engineering, and take clear ownership of model quality and business outcomes. Financial domain knowledge, statistical rigor, and the ability to translate ambiguous business questions into solvable ML problems are equally important as technical depth.
What will you do?
- Collaborate with key business partners and stakeholders to understand business objectives/opportunities and problem statements in order to provide solutions that align to business needs that are actionable with a tangible outcome
- Frame ambiguous business problems into well\-defined ML and AI problem statements with measurable success criteria.
- Own end\-to\-end model development — feature engineering, training, evaluation, and production handoff.
- Build and evaluate LLM\-augmented workflows — combining classical ML signals with generative AI where appropriate
- Prepare and transform data (structured/non\-structured)
- Design and maintain offline and online evaluation frameworks — ensuring model quality before and after deployment
- Prepare, integrate large and varied datasets and implement statistical and ML models using Python and R.
- Leverage visualization tools/packages to story\-tell and to convey data\-driven insights with actionable recommendations to key stakeholders
- Quickly learn new methods, tools and technologies presented in research communities to implement, adapt and innovate
- Effectively communicate findings to business partners and executives.
- Developing predictive data models, quantitative analyses and visualization of targeted, big data sources.
- Lead and mentor junior Data Scientists throughout the ML lifecycle.
- Monitor production models for drift and performance. Build dashboards and communicate insights.
- Document experiments and support AI governance. Present findings to technical and business stakeholders.
What do you need to succeed?
Must\-have
- Master’s in computer science or PHD in Computer Science with Specialization in Data Science, Mathematics \& Statistics.
- 10\+ years total IT experience with 3\+ years building and deploying ML models in production environments — not just notebooks
- Experience with model evaluation rigor — holdout sets, cross\-validation, leakage prevention, business metric alignment
- Practical understanding of LLM capabilities and limitations — knows when to use generative AI vs. classical ML vs. deterministic rules
- Experience building or evaluating RAG pipelines or LLM\-augmented analytics workflows — even if not the primary architect
- Comfortable working within an enterprise LLM gateway environment — model routing, cost awareness, token management
- Worked in a regulated or compliance\-sensitive environment — model documentation, auditability, and explainability requirements
- Excellent analytical, problem solving, time management and organizational skills.
- Can distinguish when a problem needs ML vs. a simpler rule\-based approach — avoids over\-engineering
- Familiarity with LLM evaluation frameworks — RAGAS, DeepEval, LLM\-as\-judge, or equivalent golden dataset approaches.
- Understands hallucination risks and validation strategies for LLM outputs used in business\-critical decisions.
- Comfortable working within an enterprise LLM gateway environment — model routing, cost awareness, token management.
- Experience in programming, scripting languages and data visualization.
Nice to have:
- Financial services domain — wealth management, portfolio analytics, risk scoring, client segmentation, or fraud detection experience.
- Experience with NLP pipelines for financial document understanding, summarization, or entity extraction.
- Familiarity with A/B testing and causal inference for evaluating model interventions in production.
- Databricks or Snowflake ML for large\-scale feature computation and model training
- Exposure to graph\-based analytics or network analysis for relationship modeling
- MLflow, Weights \& Biases, or equivalent for experiment tracking and model registry
- Familiar with a Linux environment and shell scripting.
- Familiar with data extract, transform, and load processes with a variety of data types.
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 $100,000 \- $170,000, 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
LI\-POST
TECHPJ
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:
250 NICOLLET MALL:MINNEAPOLISCity:
MinneapolisCountry:
United States of AmericaWork hours/week:
40Employment Type:
Full timePlatform:
WEALTH MANAGEMENTJob Type:
RegularPay Type:
SalariedPosted Date:
2026\-08\-07Application Deadline:
2026\-08\-28Note: *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 $100K-$170K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 30% below the category median. Disclosed range: $100K to $170K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
RBC AI Hiring
RBC has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Jersey City, NJ, US, Minneapolis, MN, US. Compensation range: $170K - $250K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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