Manager, Data Science
Job Summary
Job Description
- Bachelor's or Master's in Computer Science, Statistics, Mathematics, Data Science, or related field
- 8+ years of experience in data science
- 2+ years in a leadership or managerial role
- Expertise in Python, R, SQL, and data science libraries (e.g., scikit-learn, pandas, TensorFlow)
- Proven experience building, validating, and deploying machine learning models
- Experience with cloud platforms (AWS, GCP, Azure)
- Familiarity with ML Ops best practices
- Strong communication and stakeholder management skills
- Ability to lead multiple projects and prioritize effectively
- Experience with big data tools (e.g., Spark, Hadoop, Snowflake)
13 skills required
Job Details
Key Responsibilities: - Qualifications: - Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field. 8+ years of experience in data science, including 2+ years in a leadership or managerial role. Strong expertise in Python, R, SQL, and data science libraries (e.g., scikit-learn, pandas, TensorFlow). Proven experience in building, validating, and deploying machine learning models, including statistical modeling, predictive analytics, and experiment design (e.g., A/B testing) Experience with cloud platforms (e.g., AWS, GCP, Azure) and big data tools (e.g., Spark, Hadoop, Snowflake). Familiarity with ML Ops best practices, including model versioning, continuous integration, testing, and deployment pipelines Strong communication and stakeholder management skills. Ability to lead multiple projects and prioritize effectively.
Lead and manage a team of data scientists, providing guidance on project planning, methodology, and technical execution.
Drive the development and deployment of scalable machine learning models and analytics solutions.
Partner with stakeholders to identify high-impact opportunities and deliver actionable insights.
Translate complex data into clear business narratives and recommendations.
Ensure data science best practices are followed in model development, validation, documentation, and monitoring.
Develop and maintain KPIs, dashboards, and reports for business performance tracking.
Recruit, onboard, and mentor data science talent, fostering a culture of innovation and continuous learning.
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