Core Responsibilities
- Perform advanced statistical analysis and develop machine learning models.
- Collaborate closely with analytics teams, including data engineers and senior data scientists.
Technical Skills
- Strong command of Python is essential; knowledge of SQL is highly preferred.
- Experience working with large database systems such as MS SQL Server, or Oracle is valued.
Professional Experience
- Minimum of three years experience in analytics roles.
- Direct involvement in at least one or two domains such as Manufacturing, Commercial, Procurement, R&D, HR, or Finance.
Manufacturing Analytics
- Emphasis on addressing analytics challenges in manufacturing, including optimizing processes and predictive maintenance.
Process Optimization
- Develop and implement analytics solutions to enhance manufacturing efficiency, streamline processes, and lower costs.
Capability Building (For Senior DSs, if any)
- Lead small teams of associate data scientists and mentor junior colleagues.
- Promote a culture of knowledge sharing and continuous learning within the team.
Key Interview Areas for Data Scientist Candidates
Technical Proficiency
- Evaluate the candidate's expertise in Python and SQL.
- Assess experience with large database systems and ability to develop statistical and machine learning models.
Manufacturing Analytics Experience
- Review hands-on experience in resolving analytics issues in manufacturing, such as process optimization and predictive maintenance.
Problem-Solving Skills
- Examine ability to perform advanced statistical analysis, create models using various learning techniques, and break down complex problems into manageable components.
Communication Skills
- Assess ability to clearly convey complex analytical and technical concepts to both technical and non-technical audiences.
Team Leadership and Collaboration
- Evaluate experience in leading small teams, working with senior data scientists, and collaborating effectively in agile, multi-stakeholder environments.
Capability Building
- Look for evidence of mentoring junior team members, promoting knowledge sharing, and fostering a culture of ongoing learning and innovation.
