Senior Data Scientist, Fraud Detection & Optimization

RBC; Toronto; On-site; CAD 90,000 - 120,000; Full time

Job summary

A major financial institution in Toronto is seeking a Senior Data Scientist to develop and execute fraud detection strategies. The ideal candidate will have over 3 years of experience in analytics and be skilled in R, SQL, and Python. Responsibilities include implementing analytical methodologies, collaborating on machine learning projects, and optimizing fraud detection strategies. This position offers opportunities for innovation within the fraud management team.

Qualifications

• 3+ years’ experience in analytics or data science related roles.
• Strong practical knowledge of analytical software packages.
• Ability to effectively communicate analytical recommendations.

Responsibilities

• Implement advanced analytical methodologies for fraud detection.
• Collaborate on machine learning and AI projects.
• Develop and optimize fraud detection strategies.

Skills

Analytical skills

Communication skills

Critical thinking

Problem-solving

Education

Degree in mathematics, statistics, computer science or related quantitative discipline

Tools

R

SQL

Python

Hadoop

Hive

Spark

Scala

Job description

Job Description

As a Senior Data Scientist, Fraud Detection & Optimization, you will be responsible for the development and execution of fraud detection strategies using advanced analytical methodologies and techniques. These fraud detection strategies will be applied with the goal of reducing RBC’s Fraud Risk and losses, while maintaining a positive client experience and controlling operating costs. You will innovate and implement novel methodologies to improve detection and efficiency across the team, further automating and streamlining processes. You will contribute to and support a variety of projects and initiatives across Fraud Management and collaborate with multiple stakeholders at varying levels of seniority.

What will you do?

• Implement advanced analytical methodologies and proactively identify opportunities to innovate new techniques for fraud detection strategy creation and process and technology improvements
• Collaborate with Data Science & Innovation on machine learning and artificial intelligence projects that impact fraud detection, document methodology and standardize process to ensure repeatability
• Develop, maintain and optimize fraud detection strategies for non-traditional channels & products, and explore multiple data sources to proactively identify emerging fraud trends and patterns
• Leverage reporting to monitor the performance of existing fraud detection strategies, optimize these strategies and use analytical tools to improve detection strategies, enhancing the tools & techniques
• Prioritize fraud detection alerts across detection platforms, verify that all rule deployments are accurate and ensure a balance between fraud losses, client experience and operational costs
• Contribute to Fraud Management projects and initiatives, provide detection recommendations and implement detection strategies by project deadlines
• Develop Fraud Detection analytics best practices, contribute to the development and implementation of the rule strategy framework and create programming code standards for fraud detection
• Collaborate across the team to ensure best practices are applied, act as a technical resource for other team members and respond to inquiries regarding fraud data and detection strategies

What do you need to succeed?

Must-have

• 3+ years’ experience in analytics or data science related roles, working with large, real-world datasets
• Strong practical knowledge of, and proven experience with, analytical software packages and various programming languages (eg: R, SQL, Python etc.)
• Strong critical thinking and analytical skills with the ability to solve complex problems and develop data-driven solutions
• Exceptional communication and presentation skills, including the ability to effectively communicate analytical recommendations to technical and non-technical audiences
• Thought leadership capability to lead complex initiatives and discussions driven by analytical and business insights
• Ability to see the big picture and guide groups with strategic thinking
• Excellent time management skills and demonstrated ability to prioritize workload effectively
• Degree in mathematics, statistics, computer science or related quantitative discipline

Nice-to-have

• Experience with Hadoop, Hive, Spark, Scala or other big-data platform technology/tools
• Knowledge of Canadian banking