Data Scientist (ML) - 3 to 5 years
PhonePe (company site, opens in a new tab)3–5 yrsPuneOn-siteDirect career pageEst. 20–35 LPA
Posted 5h ago
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Job description
Structured overview of role & requirements
About This Role
- Build and deploy reusable, config-driven ML frameworks that support multiple business use cases instead of one-off models.
- Operationalize scalable models across batch, real-time and edge environments; work with engineering teams on shared data science platforms, model serving and pipeline standards.
- Monitor data ingestion and quality issues, assess downstream model impact, and provide quantitative recommendations to business and technical stakeholders.
Minimum Requirements
- Work Experience Required: JD is inconsistent—its introduction says a minimum of 1 year, while the title and required qualifications specify 3–5 years in a Data Scientist or ML Engineer role; production ML deployment experience is required.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics or a closely related quantitative field.
- Advanced Python and SQL; practical understanding of ML algorithms and statistical modeling; experience building reusable, config-driven or plugin-based systems.
- Familiarity with ML lifecycle/deployment tools such as MLflow and Docker, CI/CD for ML pipelines, modern database architectures, Git, and working knowledge of Java.
Ideal Candidate Profile
- Has taken ML models and reusable frameworks into production and can account for deployment and operationalization, not just model development.
- Fits a platform-oriented data science environment: able to turn requirements from multiple teams into shared infrastructure, standardized pipelines and adaptable frameworks.
- PySpark or other distributed-processing experience, TensorFlow or PyTorch, and computer vision are preferred shortlist signals.