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Data Scientist

4 weeks ago

toronto, Canada Arrow Workforce Solutions Full time

Tenure - Full-Time, Permanent

Title - Data Scientist

Location - Toronto, ON

Pay Range - $45/hr. - $55/hr.

Workplace Type - Hybrid

Job Summary:

We are seeking a Data Scientist to join our team of consultants and data analytics experts. This role involves developing and executing data models, with a particular emphasis on net-zero models. The ideal candidate will have a strong background in data science and a solid understanding of the ESG (Environmental, Social, and Governance) domain.

Key Responsibilities:

  • Data Analysis & Interpretation: Analyze large datasets to identify trends, patterns, and correlations, translating insights into actionable strategies.
  • Machine Learning Development: Design and deploy machine learning models for predictive analytics, optimization, and business applications.
  • Statistical Modeling: Develop statistical models to address complex business problems and enhance operational efficiency.
  • Data Visualization: Present findings using data visualization techniques such as charts, graphs, and dashboards.
  • Collaboration: Work with cross-functional teams to understand business needs, provide analytical support, and deliver data-driven solutions.
  • Quantitative Analysis: Conduct data extraction, experimentation, and presentation to formulate financial product strategies.
  • Team Support: Collaborate with product and engineering teams to support product investment and strategic decisions.

Qualifications & Skills:

  • Bachelor’s/Master’s degree in Computer Science, Computer Engineering, Mathematics, Finance, Commerce, or a related field.
  • 3+ years of experience in Data Science, ideally within the ESG domain.
  • Advanced IT skills, with certification or diploma in Information Technology.
  • Proficiency in SQL for querying relational databases (e.g., SQL Server).
  • Strong programming skills with a focus on clean, efficient, and industry-standard code.
  • Deep understanding of machine learning principles, both theoretical and practical.
  • Ability to identify and mitigate data biases effectively.
  • Strong interpersonal and communication skills, capable of explaining technical concepts to non-technical audiences.
  • Openness to diverse perspectives, fostering an inclusive and collaborative work environment.