Data Scientist 2
Publicis Groupe (company site, opens in a new tab)2–4 yrsBengaluruOn-siteDirect career pageEst. 12–20 LPA
Posted 20h ago
Reach people at Publicis Groupe
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Publicis Groupe you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop and deploy end-to-end machine learning solutions from data ingestion to production monitoring to support Automotive Retail and After Sales Service businesses.
- Apply advanced AI techniques including regression, classification, clustering, and deep learning to drive strategic decisions and improve customer acquisition, engagement, and retention.
- Automate ML workflows to enhance operational efficiency and collaborate cross-functionally to deliver scalable, production-ready models.
Minimum Requirements
- Bachelor’s or Master’s degree in Engineering or quantitative field (Statistics, Economics, Mathematics, Data Science, or Business Analytics).
- 2–4 years of relevant experience as a Data Scientist or Machine Learning Engineer.
- Proficient in Python, PySpark, SQL, and ML frameworks with a good understanding of end-to-end machine learning lifecycle and MLOps practices.
- Experience with Databricks, Cloud platforms (AWS or Azure), and working with large datasets for scalable ML solutions.
Ideal Candidate Profile
- Experience in automotive industry and familiarity with digital marketing campaigns preferred but not mandatory.
- Able to translate complex analytical insights into actionable business recommendations for stakeholders.
- Strong operational focus on scalable ML pipeline development and collaboration with cross-functional teams in a fast-paced data science environment.