Data Scientist

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Data Scientist

role at

RBC .

What is the Opportunity?

The Advanced Data Insights & Integration (ADII) Team of Personal Banking is seeking a passionate and innovative Data Scientist to join our Marketing Optimization & Planning pillar. As a Data Scientist you will design, productionize, and implement end‑to‑end marketing machine learning models and applications using state‑of‑the‑art tools and algorithms. We are looking to maintain our position as an industry leader in advanced marketing techniques and continue to drive strong client acquisition results. To achieve these goals, we leverage diverse data sources and a suite of domain‑specific machine learning models that power our client communications.

What will you do?

Design and implement production‑grade modular, configurable end‑to‑end Marketing Mix Modeling (MMM) pipelines (including data collection, feature engineering, model training, model inference, model production (forecasting / optimizations), model monitoring and model retraining / maintenance) using open‑source packages such as Meta Robyn, Google Meridian, or similar frameworks, to be scalable across multiple products and lines of business.

Prepare, parse and integrate large and varied structured and unstructured data for model training while preserving privacy and unbiased estimates; implement data quality checks, validation rules, and anomaly detection for incoming data.

Document and validate model design and performance to ensure quality and scalability of all models.

Develop custom optimization engines to recommend media budgets based on model outputs and spend constraints, and integrate with visualization platforms.

Research new capabilities and technologies to drive innovation.

Work primarily within our AWS environment (SageMaker, S3, Redshift) to accomplish business goals.

What do you need to succeed?

Must‑have

Bachelor or Master’s degree in Statistics, Computer Science, Data Science, Economics, Operations Research, Mathematics, or related quantitative field.

3–5+ years in data science, marketing analytics, or ML engineering with a proven track record building and deploying production‑grade analytics solutions end‑to‑end.

Hands‑on experience with open‑source MMM frameworks : Meta Robyn (R‑based) or Google Meridian (Python based) preferred; deep understanding of MMM concepts (adstock, saturation, decomposition, Bayesian inference).

Advanced Python proficiency : pandas, numpy, statsmodels, scikit‑learn, tensorflow / pytorch, etc.; experience with visualization (plotly, matplotlib, seaborn).

Strong SQL skills : complex queries, joins, query optimization; experience with cloud platforms (AWS S3, Glue, SageMaker or Azure / GCP equivalents) and big data frameworks (Apache Spark, Hadoop, Hive, etc.).

Working with both cloud and on‑premises notebook environments (Jupyter, Databricks, SageMaker).

Optimization expertise : building solutions using SciPy, PuLP, Pyomo, Google OR‑Tools; ability to formulate business problems as mathematical optimization problems (linear programming, convex optimization).

Experience building modular, reusable code and analytics frameworks that scale across multiple use cases; proficiency with Git, version control, CI / CD for models.

Statistical & ML expertise : regression analysis, time‑series modeling, Bayesian methods, causal inference, experimental design, propensity modelling, attrition modelling, attribution, segmentation, CLV, and behaviour analysis, supervised / unsupervised learning techniques.

Strong ability to translate business requirements into technical solutions; experience in financial services or highly regulated industries strongly preferred.

Nice to have

Experience building self‑service analytics platforms or internal tools for non‑technical users; contributions to open‑source projects related to MMM or marketing analytics.

R programming experience with tidyverse, caret, ggplot2, Shiny.

Experience with causal inference methods (difference‑in‑differences, synthetic control, instrumental variables) and geo‑experimentation or incrementality testing.

Knowledge of reinforcement learning for dynamic marketing optimization; familiarity with A / B testing platforms and experimental design.

Data engineering experience : building scalable data pipelines, API integration (REST, OAuth), orchestration tools (Airflow, Prefect).

Big data technologies : e.g. Spark (PySpark).

Containerization and deployment : Docker (basic understanding).

Advanced BI tools experience : Tableau, with automated data refresh and integration capabilities.

Understanding of marketing channels, and digital ad tech platforms (DSPs, DMPs, Google Marketing Platform), and marketing technology stacks.

Certifications : AWS / Azure / GCP, Google Analytics, marketing platform certifications; publications or conference presentations in marketing science.

What’s in it for you?

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.

A world‑class training program in financial services.

Flexible work / life balance options.

Additional Information

Address :

RBC WATERPARK PLACE, 88 QUEENS QUAY W : Toronto

City : Toronto

Country : Canada

Work hours / week : 37.5

Employment Type : Full Time

Platform :

PERSONAL & COMMERCIAL BANKING

Job Type : Regular

Pay Type : Salaried

Posted Date : 2025-12-04

Application Deadline :

2025-12-31 (Applications will be accepted until 11 : 59 PM on the day prior to the application deadline date).

Inclusion and Equal Opportunity Employment

At 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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