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Int Data Science Engineer with strong Python, data modelling, GCP, and Apache Airflow to build an AI analytics product.

Job Type: Permanent
Positions to fill: 1
Start Date: Jun 27, 2022
Job End Date: Jun 27, 2022
Pay Rate: Salary: Negotiable
Job ID: 120309
Location: Toronto
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Our client is a leader in the Venture Capital Space specifically focused on investing in FinTech Startup’s. They are looking for a Int Data Science Engineer with strong Python, data modelling, GCP, and Apache Airflow to build an AI analytics product.

Location: Remote

Project: Build an AI Product used to determine viable start up portfolio additions to our clients growing list of start up investments.

Must Haves:
  • GCP technologies
  • Build and maintain existing Apache Airflow DAGs
  • Expert Python development skills GCP big Query skills, grab data and store it. Used Tensor flow
  • Building and maintain data APIs that power web applications and data dashboards
         
Responsibilities:
  • Prototype new technology that supports our vision of making our consumer’s experiences better with our data
  • Design, implement and maintain data pipelines for extraction, transformation, and loading of data from a wide variety of data sources using
  • Provide support and insights to the business analytics and data science teams
  • Work with stakeholders to assist with data-related technical issues and support their data infrastructure needs
  • Work with Data Scientists and Machine Learning Engineers to build out robust and scalable pipelines and APIs
  • Work on a data product that informs the investment teams of new start-up companies to invest in
  • Gather overall market intelligence, including on: startups, funding, other venture funds, exits, trends by verticals (fintech, logistics, etc.), trends by countries, macro trends, etc.
  • Design and implement work flows, procedures, and software to automate and improve data collection
  • Streamline Human-CRM interactions via processes and automation
  • Uses research, intuition, and experience to complement data