Data Scientist(Ai)

**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**

Dimensions

Operating Budget

Number of Staff

Capital Exp. Budget

Other

**4**

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.

Pay: ₹200,000.00 - ₹250,000.00 per month

**Experience**:

- Data Scientist: 10 years (required)
- AI: 10 years (required)
- ML: 10 years (required)
- Python: 1 year (required)
- TensorFlow: 10 years (required)
- NLP: 10 years (required)
- Azure: 10 years (required)
- Banking: 10 years (required)
- Financial: 10 years (required)
- LLM: 10 years (required)

Work Location: Remote

Application