Senior NLP Data Scientist: Architect & Deploy AI Solutions

inDrive; Pakistan; On-site; PKR 3,000,000 - 6,000,000; Full time

Benefits offered by this job

Mentoring & career development

Global Talent Exchange Program

International collaboration

Job summary

inDrive is seeking a Senior Data Scientist for its AI Cluster in Pakistan. You will architect, build and deploy production-grade NLP models to automate customer support operations, tackling real-world problems with scalable solutions.

You will lead the model lifecycle, mentor teammates, and collaborate with backend teams to integrate ML systems while ensuring performance and security in a production environment.

Qualifications

• 5+ years of experience in data science or ML with NLP focus.
• Strong Python and core data science libraries proficiency.
• Experience with ML systems, deployment and monitoring (MLOps).
• Quantitative background: CS/Math or related a plus.
• Software engineering experience preferable.

Responsibilities

• Lead ML model lifecycle from research to production deployment.
• Translate business goals into data science problems and metrics.
• Design scalable ML systems and data pipelines.
• Integrate ML models with backend services and infrastructure.
• Monitor deployed models and address concept drift.
• Mentor teammates and assist onboarding.
• Drive automation and innovative solutions for business impact.
• Communicate complex results to technical and non-technical stakeholders.

Skills

Python

NLP

ML system design

MLOps

Data pipelines

Experimentation

Production monitoring

Streaming / batch processing

Software architecture

Security & risk

Education

Bachelor’s or higher in CS/Math/related

Tools

Pandas

NumPy

Scikit-learn

Job description

inDrive is seeking a Senior Data Scientist for its AI Cluster in Pakistan. You will architect, build and deploy production-grade NLP models to automate customer support operations, tackling real-world problems with scalable solutions.

You will lead the model lifecycle, mentor teammates, and collaborate with backend teams to integrate ML systems while ensuring performance and security in a production environment.