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inDrive's AI Solution Delivery team is expanding its NLP capabilities to build production-grade AI products for customer support automation. As a Senior Data Scientist, you will architect, build, and deploy complex machine learning models that automate customer support operations at scale.
You will own the entire ML model lifecycle—from initial research and hypothesis testing through production deployment and ongoing maintenance. Your responsibilities include translating business objectives into well-defined data science problems with quantifiable metrics, designing and developing robust scalable ML systems from scratch (including data analysis, annotation, and processing pipelines), and integrating ML models with existing backend services and infrastructure.
You will monitor deployed models to identify and address issues like concept drift, ensuring consistent performance in production. Beyond individual contribution, you will mentor team members and participate in onboarding programs to support team growth. You will drive continuous improvement by automating repetitive tasks and proposing innovative solutions with measurable business impact. Clear communication of complex technical concepts to both technical and non-technical stakeholders is essential.
REQUIREMENTS:
- 5+ years of professional experience in data science or machine learning with deep focus on NLP
- Previous software engineering experience preferred
- Academic background in Computer Science, Mathematics, or related quantitative field (a plus)
- Expert-level proficiency in Python and core data science libraries (Pandas, NumPy, Scikit-learn)
- Deep expertise in classic machine learning and deep learning techniques with strong understanding of advanced mathematics
- Experience with ML system design and MLOps practices for building, testing, deploying, and monitoring models in production
- Proven experience with event systems, deployment environments, and maintaining production services
- Familiarity with technologies for streaming, batch, and async data processing
- Strong understanding of software system design principles and ability to contribute to architectural discussions
- Experience in experimental design to validate hypotheses and measure solution effectiveness
- Solid grasp of security, risk, and control concepts in production environments
