Deskripsi Pekerjaan
NielsenIQ is the world's leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. As a Senior Data Scientist in our Kuala Lumpur City Centre office, you will play a pivotal role in shaping the future of retail analytics.
In this high-impact position, you will develop end-to-end analytic solutions that transform raw data into strategic business insights. You will work closely with global teams to design algorithms, build predictive models, and drive data-driven decision-making for top FMCG brands. We are looking for a technically brilliant and curious innovator who thrives on solving complex problems at scale.
This is an opportunity to make a tangible impact on the global consumer goods ecosystem while working with petabytes of rich retail data. You will have access to cutting-edge tools and a collaborative environment that supports professional growth and innovation.
Location: Kuala Lumpur City Centre, Kuala Lumpur, Malaysia.
Tanggung Jawab
- Develop and deploy advanced machine learning models and statistical algorithms to solve complex business problems.
- Build end-to-end data pipelines and analytic solutions using Python, SQL, and cloud platforms.
- Collaborate with product managers, engineers, and data architects to design and implement scalable analytics systems.
- Conduct deep exploratory data analysis to uncover consumer behavior patterns and market trends.
- Design and analyze A/B tests and experiments to drive product and marketing strategies.
- Communicate insights and recommendations to executive stakeholders through compelling data visualizations and narratives.
- Mentor and guide junior data scientists, fostering a culture of technical excellence and collaboration.
Kualifikasi
- Master's or PhD in Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative field.
- 5+ years of experience in a Data Science or Machine Learning role.
- Expert proficiency in Python and SQL. Experience with PySpark and cloud ML services (AWS, GCP, Azure) is a strong plus.
- Deep understanding of statistical modeling, machine learning techniques (e.g., Regression, Classification, Clustering, NLP, Deep Learning), and experimental design.
- Proven track record of deploying production-grade machine learning models.
- Experience working with large-scale retail, FMCG, or consumer panel data is highly desirable.
- Excellent communication and presentation skills, with the ability to translate complex technical concepts to diverse audiences.
- Strong problem-solving skills and a passion for data-driven innovation.