Lead Data Scientist [up to RM16k]
Summary
Lead a team of data scientists to build predictive models and statistical algorithms using Python/R and SQL, guiding projects from scope to delivery.
Benefits
- Excellent Compensation Package
- Accessible Location
- Exciting Projects
About the Company
Randstad has partnered with a local software solutions company, offering solutions within analytics and consultation services for their clientele. Your future employers prioritize a problem solving mentality in delivering success.
Key Responsibilities
- Lead and Mentor the Team: Manage a dedicated team of data scientists, providing technical guidance, conducting code reviews, and fostering professional growth and skill development.
- Oversee Project Lifecycles: Act as the project manager for analytics initiatives, defining project scopes, setting timelines, allocating resources, and ensuring high-quality, on-time delivery.
- Develop Predictive Models: Design, build, and deploy high-performing predictive models and statistical algorithms to forecast trends and optimize business outcomes.
- Apply Rigorous Statistics: Utilize advanced regression techniques (such as linear, logistic, and generalized linear models), hypothesis testing, and data validation to ensure the highest level of model accuracy.
- Collaborate with Stakeholders: Translate complex statistical findings and data insights into clear, actionable business recommendations for executive leadership and non-technical teams.
Requirements
- Years of Experience: 5+ years of professional experience in data science, advanced analytics, or statistical modeling roles.
- Proven Leadership: Demonstrated track record of successfully managing data professionals and leading cross-functional data projects.
- Educational Background: Master’s degree or higher in Statistics, Mathematics, Economics, or a highly quantitative field of study.
- Core Technical Stack: Proficiency in Python or R (specifically libraries like Pandas, Statsmodels, and Scikit-Learn) alongside advanced SQL skills for data extraction.
- Methodological Expertise: Strong foundational knowledge of classical statistical theory, experimental design, regression analysis, and data engineering principles.