Data Scientist
About the Role
We are seeking an experienced Data Scientist to design, develop, and deploy advanced machine learning and artificial intelligence solutions that address complex business challenges. The successful candidate will work across the full data science lifecycle, leveraging structured and unstructured data to build scalable, production-ready models and deliver measurable business value.
Working closely with Engineering, Product, and DevOps teams, you will contribute to the development of innovative AI-driven products and solutions, ensuring best practices in model development, deployment, and operationalization.
Key Responsibilities
- Design, develop, and deploy machine learning and deep learning models to solve business and operational challenges.
- Apply advanced statistical, machine learning, and AI techniques to extract insights from structured and unstructured datasets.
- Perform data acquisition, exploration, cleansing, preprocessing, and feature engineering activities.
- Evaluate and select appropriate algorithms, architectures, and modelling approaches based on business requirements.
- Build, optimize, and maintain end-to-end machine learning pipelines.
- Fine-tune and enhance model performance through experimentation, testing, and continuous improvement.
- Identify new data sources and automate data collection and preparation processes where possible.
- Develop solutions across predictive analytics, natural language processing (NLP), and AI-driven automation use cases.
- Integrate machine learning models into production environments using APIs and containerized deployment approaches.
- Collaborate with engineering and DevOps teams to support model deployment, monitoring, and operational excellence.
- Contribute to research and innovation initiatives, evaluating emerging AI and machine learning technologies.
- Work closely with stakeholders to translate business requirements into scalable data science solutions.
- Present insights and technical recommendations to both technical and non-technical audiences.
- Support solution rollout, user adoption, and ongoing optimization activities.
Required Qualifications
- Bachelor's degree in Data Science, Artificial Intelligence, Computer Science, Mathematics, Engineering, Business Analytics, or a related discipline.
- Relevant industry certifications in AI, Machine Learning, Data Science, or Cloud Technologies are advantageous.
Experience
- 7–10 years of experience in Data Science, Machine Learning, Artificial Intelligence, or related analytical roles.
- Proven experience delivering machine learning solutions in enterprise environments.
- Demonstrated experience working across the complete machine learning lifecycle, from data preparation through deployment and monitoring.
Technical Skills
Machine Learning & AI
- Strong understanding of machine learning and deep learning concepts and methodologies.
- Experience developing models for both structured and unstructured datasets.
- Hands-on experience with:
- TensorFlow
- PyTorch
- Scikit-learn
- Knowledge of model evaluation, optimization, and performance tuning techniques.
Natural Language Processing (NLP)
- Hands-on experience building NLP solutions using:
- Hugging Face
- spaCy
- Gensim
- Experience working with transformer-based architectures and modern NLP frameworks.
Programming & Data Engineering
- Advanced Python programming skills.
- Knowledge of Java or R is desirable.
- Strong experience with:
- Experience handling large-scale datasets and distributed data processing.
Deployment & MLOps
- Experience developing and exposing models through REST APIs.
- Experience with Docker and containerized deployments.
- Familiarity with ML lifecycle and experiment-tracking tools such as MLflow.
- Understanding of CI/CD processes and production deployment best practices.
Desirable Skills
- Experience working with Large Language Models (LLMs) and Generative AI solutions.
- Exposure to offline and on-premises AI deployment environments.
- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
- Familiarity with Jupyter Notebooks, Git, and collaborative development workflows.
- Experience with business intelligence and data visualization tools.
- Industry experience within Financial Services, Banking, FinTech, Education, or related sectors would be advantageous.
