Senior Data Scientist (NLP)

inDrive

Lead the entire machine learning model lifecycle, from initial research and hypothesis testing to production deployment and maintenance

• Translate complex business goals into well defined data science problems and quantifiable metrics
• Design and develop robust, scalable machine learning systems from scratch, including data analysis, annotation, and processing pipelines
• Contribute to the overall system architecture and integrate ML models with existing backend services and infrastructure
• Monitor and maintain deployed models, proactively identifying and addressing issues like concept drift to ensure consistent performance
• Support the development and growth of other team members through mentorship and participation in onboarding programs
• Drive continuous improvement by automating repetitive tasks and proposing innovative solutions that lead to significant business impact
• Communicate complex technical concepts and findings clearly and concisely to both technical and non technical stakeholders

Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 48 countries.

Develop your professional skills with access to mentoring, career consulting, and learning programs.

Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.

Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.

Work alongside people who take initiative, speak openly, and challenge themselves to grow.

Improve your language skills through co-financed courses and internal speaking clubs.

5 years of professional experience in a data science or machine learning role with deep focus on NLP.

Previous software engineering experience is preferrable.

Expert level proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit learn).

Deep expertise in classic machine learning and deep learning techniques, with a strong understanding of advanced mathematics relevant to these fields.

Experience with ML system design and MLOps practices for building, testing, deploying, and monitoring models in a production environment.

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 the ability to contribute to architectural discussions.

Experience in experimental design to validate hypotheses and measure the effectiveness of solutions.

A solid grasp of security, risk, and control concepts in a production environment.