Data Scientist (AI)

About the job

**Field** • *Details** • *Job Title** • *Data Scientist (AI)** • *Location** • *Remote -Offshore India** • *Skill Set** • *Data Science, AI/ML, Banking Domain, Cloud** • *Duration** • *6 Months** • *Shift Hours** • *General** • *1** • *Job Title** Data Scientist (AI) • *2** • *Job Purpose** 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. • *3** • *Key Result Areas** - Collaborate with business units to identify high-impact AI use cases. - Design and build scalable machine learning models and AI solutions. - Perform data extraction, preprocessing, feature engineering, and model validation. - Evaluate model performance and ensure robustness, fairness, and explainability. - Work with MLOps teams to deploy models into production environments. - Monitor and retrain models as needed to ensure continued effectiveness. - Translate complex findings into actionable insights and communicate clearly to non-technical stakeholders. - Stay current with the latest research and best practices in AI/ML. - Oversee production of the processes and outputs to meet stakeholders’ needs and requirements. - Assemble large, complex data sets that meet functional / non-functional business requirements. • *5** • *Operating Environment, Framework and Boundaries, Working Relationships** - Able to Effectively Plan & Organize Their Work - Strong Interpersonal Communication - Assist others in completion of their tasks to support the group goals. - Build and maintain cooperative work relationships with others. • *6** • *Problem Solving** - **Translate complex business challenges into AI/ML solutions** by identifying the right algorithms, tools, and frameworks suited for each use case. - Design **end-to-end AI pipelines** — from data acquisition to model deployment — to solve real-world problems with measurable impact. - Leverage domain knowledge to **prioritize high-impact AI opportunities**, ensuring alignment with business goals and ROI. - Troubleshoot and resolve technical issues related to data quality, model drift, and performance bottlenecks across cloud-based AI environments. - Implement fail-safes and fallback logic for mission-critical AI models to ensure resilience and uptime. • *7** • *Decision Making Authority & Responsibility** - Work on the right tasks by ensuring they know their top deliverables. - Assume personal responsibility for achieving outcomes. • *8** • *Knowledge, Skills, and Experience** • *Domain Expertise**: - 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**. • *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.

Requirements

• Data Science
• AI/ML
• Banking Domain
• Cloud

Preferred Technologies

• Data Science
• AI/ML
• Banking Domain
• Cloud