IBM Data Scientist (Client Insights) in TORONTO, Ontario
We are a small, diverse team of passionate data scientists and developers who deliver a leading-edge analytic solution to IBM clients in the financial services sector via cloud. We offer flexible working arrangements, team spirit, and a learning-first environment in an agile framework. By joining our team, a data scientist would be exposed to "production quality" algorithms and processes, contribute to the IP and quality of the solution directly, participate in team-planning activities and work directly with clients periodically to ensure relevance of our models. Mentoring is provided on all aspects of the data science process from data modeling to feature identification to algorithm application, from coding styles to quality assurance and testing. We are an innovative and creative part of IBM's Watson Financial Services Sector team.
In this role you will work regularly with developers, quality assurance, offering management and research. Other teams such as support, operations, sales and services will be critical interaction points from time to time.
Job Responsibilities will include but are not limited to:
Build models as needed: descriptive, predictive, prescriptive or cognitive
Assess, document and articulate the effectiveness of developed models
Enhance, document and curate a common data model to support all analytics
Create examples and use cases to support solution engagement and sales opportunities
Work with client data to showcase the use and flexibility of cloud-based analytics models (and then improve those models based on experience gained)
Identify approaches to improve the accuracy and robustness of analytics models
Create visual and/or written presentations of analytics results and translate quantitative insights for a non-technical audience
Work with a variety of experts in architecture, development and the financial services area.
Refine, analyze and structure relevant data
Required Technical and Professional Expertise
Master's Degree in a quantitative discipline
At least 5 years’ experience in one of the following fields: mathematics, statistics, operations research, engineering, economics or quantitative finance.
Exposure to the financial services industry (2+ years)
Programming or scripting skills for data science (e.g., R, python, Scala, SPSS) (2+ years)
Experience with classification, recommender systems and basic neural net use cases (3+ years)
Demonstrated familiarity with Spark and big data / distributed computing concepts (1+ years)
Preferred Tech and Prof Experience
PhD in Mathematics or Statistics or similar quantitative discipline
10 years’ experience in one of the following fields: mathematics, statistics, operations research, engineering, economics or quantitative finance.
5 years’ experience working in for with the financial services industry
Hands on experience with deep learning (2 years)
Strong programming or scripting skills for data science (esp. Spark) (2 years)
At least 1 years’ experience in identifying and defining requirements and turning functional requirements into a predictive or prescriptive analytics solution that addresses difficult business problems.
Experience working with financial data (e.g., stock market, retail banking, asset management or insurance premia) (3 years)
Demonstrated knowledge of relational, nosql and dimensional data modeling (2+ years)
IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.