Senior Data Scientist
inDrive Open · verified Oct 24, 2026 Hybrid Islamabad Full-time Senior Data Science
At a glance
Senior Data Science role at inDrive. Islamabad · full-time.
Pay not stated Data Scientist salary
RoleData Science
SenioritySenior
LocationIslamabad
WorkplaceHybrid
EmploymentFull-time
PostedSep 7, 2026 · 6w ago
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Lead NLP model work from early research and testing through launch, monitoring and ongoing upkeep
- Turn customer support goals into data science questions and measurable success criteria
- Build scalable ML systems, including analysis, data labeling and processing workflows
- Connect models to backend services and infrastructure, and help shape the wider system design
- Track model performance, address concept drift, mentor teammates and explain findings to varied audiences
What you bring
- At least five years in data science or machine learning, with a strong focus on NLP
- Advanced Python skills, including core data libraries such as Pandas, NumPy or Scikit-learn
- Hands-on experience designing, deploying and monitoring ML systems in production
- Knowledge of machine learning, deep learning, experimental design and production system architecture
Who this fits
This senior role suits someone who can take NLP models from research into production and contribute to system design. It includes mentoring teammates and communicating with both technical and non-technical stakeholders. The role is hybrid in Islamabad.
From the employer
The AI Solution Delivery team is expanding its capabilities in NLP to build production-grade AI products. We are looking for a Senior Data Scientist to architect, build and deploy complex models for automating customer support operations. You will be responsible for selecting the right technical approaches and ensuring the scalability of our solutions. A proven track record in applying advanced NLP techniques to real-world problems is required.
- 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
- 5 years of professional experience in a data science or machine learning role with deep focus on NLP.
- Previous software engineering experience is preferrable.
- An academic background in a quantitative field such as Computer Science, Mathematics, or a related discipline will be a plus
- 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.
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