Lead Data Scientist

What is the opportunity?

The Personal Banking & Commercial Banking Data Strategy team is looking for a passionate and innovative Senior Data Scientist within our data asset team. As a Data Scientist you will train, analyze, design, implement and monitor machine learning models and applications using state of the art tools and algorithms. You will collaborate with a highly talented team of data engineers, data scientists, data architects, and data analysts to deliver business value to the organization. You will develop recommendations for new ways to improve data quality throughout the data transformation lifecycle.

What will you do?

• Design, build, validate, deploy, and analyze advanced models using machine learning, AI, and statistics end-to end, including feature engineering, model training, model inference, model production, model monitoring and model maintenance.

• Work with big data, and cloud technologies such as Spark, Hadoop, and AWS environment (SageMaker, S3, Redshift)

• Join data across multiple data environments, such as HDFS, S3 and Data Warehouses, using SQL queries with complex joins, groupings, and aggregations.

• Partner with RBC internal partners and lines of businesses (Digital Marketing, Programmatic Media) to help them frame use cases and define success metrics (Key Performance Indicators).

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

• Monitor model performance and business success metrics over time.

• Be responsible for researching new capabilities and technologies to drive innovation.

• Work closely with development teams to learn about needs, current processes and to promote best practices.

What do you need to succeed?

Must Have

• Master's degree in Statistics, Economics, Marketing (Quantitative Workstream) Mathematics, or related quantitative field

• 5+ years of hands-on experience or similar post-grad work in applying Machine Learning to Customer Marketing

• Strong foundation in statistics, probability, and machine learning (including deep learning), especially in areas like: Causal inference, regression analysis, hypothesis testing, Bayesian inference, time series forecasting, propensity modelling, attrition modelling, attribution, segmentation, CLV, and behavioural analytics.

• Professional software development experience with Spark, Hadoop, AWS Cloud (SageMaker, S3, Redshift), GitHub and SQL

• Strong programming skills in python (preferred), including relevant statistical, machine learning, and deep learning libraries (pandas, numpy, stats models, scikit-learn, tensorflow/pytorch, etc.)

• Able to clearly convey technical concepts and results to both technical and non-technical audiences, including senior leadership.

Nice to Have

• Experience in running A/B testing experiments in production. Experience with relational databases. Domain specific knowledge of marketing campaigns.

• Experience in financial services industry with broad understanding of marketing, product management, sales, finance, pricing and risk management is preferred.

• Publications in machine learning and artificial intelligence.

• Passion for ethical AI and experience in algorithm transparency and interpretability.

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 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.

• Work in a dynamic, collaborative, progressive, and high-performing team.

• A world-class training program in financial services

• Flexible work/life balance options

• Opportunities to do challenging work, to take on progressively greater accountabilities and to building close relationships with clients.

• Access to a variety of job opportunities across business and geographies.

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