Senior Data Scientist
New
J
JobgetherTechnology
CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonData AnalysisGitMachine LearningJiraData scienceConfluence
Requirements
- Bachelor’s or Master’s degree in Data Science, Computer Science, Computational Sciences, or a related field.
- 3+ years of relevant industry experience as a Data Scientist in a fast-paced, high-growth technology environment.
- Proven experience working with large tabular and user-behavior datasets.
- Experience translating product or machine learning objectives into concrete data requirements and specifications.
- Strong understanding of data science fundamentals, statistics, and mathematics.
- Fluency in Python, Git, and Unix shell environments.
- Familiarity with collaborative and engineering tools such as Jira, Confluence, Slack, Git-based workflows, and experiment tracking frameworks.
- Strong analytical and problem-solving skills with exceptional attention to detail.
- Demonstrated commitment to data accuracy, integrity, and quality.
- Excellent communication and collaboration skills.
Responsibilities
- Collaborate with ML engineers, Product teams, and Technical Program Managers to define data and model training requirements.
- Develop and oversee data strategies for curating, filtering, processing, and preparing large tabular and behavioral datasets.
- Translate high-level product and machine learning objectives into clear, actionable data specifications for engineering and annotation teams.
- Ensure datasets are structured and aligned with product objectives and machine learning requirements.
- Analyze behavioral data to assess its accuracy, representativeness, completeness, and suitability for model training.
- Maintain high standards of data integrity and quality throughout the data preparation lifecycle.
- Partner with cross-functional teams to identify data gaps, inconsistencies, and opportunities for improvement.
- Help establish scalable processes and requirements for production machine learning datasets.
- Communicate technical data requirements effectively across different engineering and product disciplines.
- Contribute to data science practices, experimentation, and analytical approaches that support evolving product and ML initiatives.
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