We are looking for a hands-on
Data Scientist with strong technical expertise and a passion for building AI-driven solutions that create real business value. In this role, you will be at the forefront of transforming business problems into scalable AI/ML models using tools such as
Python,
TensorFlow,
PyTorch,
Hugging Face,
Scikit-learn, and
Spark. The ideal candidate will work with structured and unstructured data, leveraging
cloud platforms (e.g.,
Vertex AI, or
Azure ML) and integrating models into production environments using
MLOps frameworks like
MLflow,
Kubeflow, and
Airflow.
He will collaborate with cross-functional teams including product managers, data engineers, and domain experts to identify opportunities for AI, rapidly prototype models, and deploy solutions on a scale. A strong grasp of
NLP,
large language models (LLMs), and
generative AI is a big plus, especially for use cases involving customer experience, automation, and intelligent decision systems.
This role blends data science rigor with real-world applicationperfect for someone who thrives at the intersection of innovation, technology, and business impact.
Domain Expertise
Knowledge, Skills, and Experience
- Proven experience in implementing AI solutions within banking or finance sectors.
- Strong understanding of data privacy, compliance, and ethical AI principles.
AI & Machine Learning
- Proficiency in building and deploying machine learning, natural language processing (NLP), and generative AI/LLM models using libraries such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face.
- Experience with LLMs (Large Language Models) and their application in use cases like chatbots, document summarization, intelligent search, and virtual assistants.
Cloud & Azure Technologies
- Deep hands-on experience with Azure Machine Learning (AML) for developing, training, and operationalizing ML models.
- Skilled in using Azure Cognitive Services (e.g., Language Understanding, Computer Vision, Speech, and Text Analytics) to create intelligent AI capabilities.
- Familiarity with Azure Databricks for scalable data processing and collaborative ML development.
- Expertise in integrating AI workflows with Azure Data Lake, Azure Synapse Analytics, and Azure Data Factory.
- Working knowledge of the Azure OpenAI Service, including model fine-tuning and deployment.
- Experience with Azure DevOps and CI/CD pipelines for model lifecycle automation.
- Implementation experience of real-time AI solutions using Azure Event Hubs, Azure Stream Analytics, and Azure Functions.
MLOps & Model Lifecycle
- Practical experience with MLflow, Kubeflow, and Apache Airflow for model orchestration and tracking.
- Familiarity with tools like InterpretML, and Azure Responsible AI dashboards for bias detection, interpretability, and model governance.
- Knowledge of model versioning, monitoring, and lifecycle management using Model Registry and Azure ML's native tools.
