Senior Data Scientist

This is a hybrid position requiring presence in Vancouver, BC.

What You'll be doing:

• Create data-driven insights in order to enrich the end user experience

• Communicate with multiple teams by creating reports in order to share insights and overall data story

• Work alongside senior level stakeholders to scope analytics projects to ensure data science deliveries are driving business impact

• Evaluate consumer fintech data by using various methods and tools including but not limited to; Machine Learning, Statistics, Data Analysis, Quantitative Finance, and Modern Programming

• Facilitate data driven initiatives in the following areas to paint a cohesive data picture:

  • Customer Segmentation

  • Underwriting

  • Lifetime Value Optimization

  • Financial product development

  • Fraud and Ethical Lending Analysis

What You Should Already Have:

• A Bachelor’s degree in a quantitative field, such as Finance, Computer Science, Economics, Mathematics or a similar technical discipline

• 5+ years experience in applying analytics, data engineering or data science

• Proficiency in working with SQL, Python and/or R across complex real world datasets

• Proficiency in using modern data visualization tools, such as Tableau, PowerBI or similar

• Experience deploying ML models using machine learning as a service platforms such as Amazon SageMaker, Google Cloud AI, and Microsoft Azure

• Excellent written and verbal communication skills

• Aptitude for critical thinking as it relates to complex problems and challenge assumptions in order to implement creative solutions

What Would Be Nice To Have:

• A Master's degree or higher, focused in data science, analytics finance, or software engineering

• Experience applying data science consumer finance, fintech and other Financial Industry domains

• Familiarity with related disciplines such as consumer finance, project

• management, user experience design, software development and digital

• marketing

• Experience in establishing data technology foundations for agile business

• intelligence, data lakes and data workbenches, and applied machine learning/AI

• Experience with large-scale data analysis technologies (e.g. Relational and

• NoSQL data stores, Spark framework, and cloud development in the AWS

• Ecosystem)

• Specializations and applied experience in the following areas:

  • Consumer Credit and Risk Modeling

  • Fraud and Anomaly Detection

  • Customer Segmentation and Intent Modeling

  • Marketing Attribution and Targeting

  • Recommendation Engines

Originally posted on Himalayas