US Wealth Management Data Scientist, RBC Capital Markets, LLC, Minneapolis, MN:

Royal Bank of Canada•Minneapolis, MN

7h•Hybrid

About The Position

Analyze, design and implement data science / machine learning (“ML”) solutions using RBC’s enterprise suite of analytics tools. Reviewing large data sets to explore and discover new insights that would have not been possible with traditional analytics. Leveraging leading edge technologies and capabilities, this role applies machine learning and statistical modelling techniques to help RBC US Wealth Management understand the changing business environment, discover new growth opportunities and determine where business improvements can be made. The role will 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. Prepare and transform data (structured/non-structured). Develop and deploy ML solutions at scale. 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. Telecommuting permitted up to 2 days per week. #LI-DNI Full time employment, Monday – Friday, 40 hours per week, $142,230 per year.

Requirements

Must have a Bachelor’s degree or foreign equivalent in Statistics, Mathematics, Computer Science, or related field and 5 years of progressive post-baccalaureate related work experience. Alternatively, the employer will accept a Master’s degree or foreign equivalent in Statistics, Mathematics, Computer Science, or related field and 3 years of related work experience.
Must have 3 years of experience in: Working in the wealth management industry, with the ability to develop analyses and models relevant for financial advisors and senior management
Creating automated Data ETL processes using scripting Languages, such as Python/SQL, to consolidate data from multiple sources and to streamline future development
Creating data features for machine learning
Automating and documenting data analysis processes using Python, including effectively transitioning completed work to a production support team, optimizing workflow and enabling the allocation of resources to additional projects
Transforming and analyzing large data sets, including historical transactional data, to develop insights and effectively identify anomalies
Design, development, and implementation experience utilizing data science projects using scripting languages (Python, Java, R, and SQL)
Ability to code, explain, and select intermediate machine learning models (Random Forest, XGBoost, and K-means)
Increasing adoption of models by educating business users on the modeling process and the reliability of models.
Employer will accept any suitable combination of education, training, or experience.

Responsibilities

Analyze, design and implement data science / machine learning (“ML”) solutions using RBC’s enterprise suite of analytics tools.
Reviewing large data sets to explore and discover new insights that would have not been possible with traditional analytics.
Leveraging leading edge technologies and capabilities, this role applies machine learning and statistical modelling techniques to help RBC US Wealth Management understand the changing business environment, discover new growth opportunities and determine where business improvements can be made.
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.
Prepare and transform data (structured/non-structured).
Develop and deploy ML solutions at scale.
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.