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Data Scientist - Greater Toronto Area, Canada - Akkodis
Description
Looking to my network for some help with finding a Data Scientist. We need someone who is strong with SQL, Spark, and relational databases technologiesas , 10+ years of production experience with statistical analysis, machine learning, and digital analytics (e.g., hypothesis-driven analysis, acquisition, and fraud/anomaly detection)3-5+ years' experience with Power BI and/or Tableau, well has experience working for a financial institution Message me if you are available
Title: Data Scientist - 5
Location: Hybrid - 44 King Street W 23rd Floor, Toronto – 2x/month according to meetings, need candidates in GTA
Client: Banking
Candidate Requirements/Must Have Skills:
1) 10+ years of combined working experience with SQL, Spark, and relational databases technologies/concepts for both (please list which)
2) 10+ years of production experience with statistical analysis, machine learning, and digital analytics (e.g., hypothesis-driven analysis, acquisition, and fraud/anomaly detection)
years' experience with Power BI and/or Tableau (Power BI preferable)
Nice-To-Have Skills:
1) Domain knowledge/experience with digital authentication and digital fraud analytics.
2) Hands-on experience with Big Data/Cloud ecosystem (GCP)
3) Experience with existing Know-Your-Customer (KYC) practices across financial institutions and the supporting technologies
Candidate Value Proposition:
The successful candidate will have the opportunity to gain exposure to advanced technologies in big data analytics, such as Spark, GCP, Kafka; work in a top 5 Canadian Bank.
Typical Day in Role:
• Collaborate with business lines and other stakeholders and identify opportunities to drive business value by leveraging data science and data engineering solutions
• Efficiently handle large volumes of structured and unstructured data through ingestion, modeling, transformation, and storage across diverse data stores. Leverage distributed computing tools (e.g., Spark, Cloud) for analysis, data mining, and modeling
• Collaborate with operation, and other analytics teams to deploy models and algorithms in production across different channels and platforms
• Work with the team to design the project architecture and road map.
• Prepare detailed documentation to outline data sources, models, and algorithms used and developed.
• Present results to business line stakeholders and help implement real world data-driven changes.
