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