We are looking for passionate and outcome-driven Data Scientists who enjoy solving complex business problems using Artificial Intelligence and Machine Learning. In this role, you will work on designing, developing, and deploying scalable AI/ML solutions that drive real business impact.
As a Senior Data Scientist, you will collaborate with engineering, product, and business teams to build production-ready machine learning models across use cases such as NLP, intelligent automation, anomaly detection, predictive analytics, and decision support systems.
Key Responsibilities
- Design, develop, and deploy end-to-end machine learning solutions.
- Build and optimize NLP and deep learning models for production use.
- Analyse large and complex structured and unstructured datasets to generate actionable insights.
- Translate business problems into scalable AI/ML solutions.
- Partner with engineering teams to deploy, monitor, and continuously improve ML models.
- Conduct model experimentation, hyper parameter tuning, and performance evaluation.
- Implement model monitoring, retraining strategies, and performance optimization.
- Present technical findings and business insights to both technical and non-technical stakeholders.
- Mentor junior data scientists and contribute to best practices across the team.
Requirements
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.
- 3–9 years of hands-on experience in Data Science, Machine Learning, Applied AI, or Advanced Analytics.
- Strong understanding of Machine Learning algorithms, Statistics, Probability, and Optimization techniques.
- Solid experience with Python (preferred) or Scala.
- Experience with model evaluation techniques, experimentation, and A/B testing.
- Strong problem-solving and analytical skills.
- Excellent communication and stakeholder management abilities.
Preferred Skills
- Experience with Natural Language Processing (NLP), Large Language Models (LLMs), or Generative AI.
- Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch.
- Knowledge of text mining, embeddings, vector databases, and transformer-based models.
- Experience deploying ML models in cloud or production environments.
- Familiarity with MLOps, model monitoring, versioning, and CI/CD pipelines.
- Understanding of model explainability and interpretability techniques.