Job Description

Role & Responsibilities

- Analyzing large datasets to identify trends, patterns, and insights.

- Using statistical methods to validate findings and ensure they are reliable.

- Performing exploratory data analysis (EDA) to understand the data's structure and characteristics.

- Developing machine learning models to make predictions or classifications based on the data.

- Selecting appropriate algorithms and techniques for specific problems.

- Training, validating, and testing models to ensure they perform well.

- Creating visualizations to communicate findings clearly and effectively to stakeholders.

- Using tools like Tableau, Qlik, or Power BI to create charts, graphs, and dashboards.

- Presenting complex data in a user-friendly manner.

- Applying statistical techniques to analyze data and draw meaningful conclusions.

- Conducting hypothesis testing and regression analysis.

- Ensuring the statistical validity of the results.

- Working with business leaders, analysts, and other stakeholders to understand their needs and requirements.

- Define project scope, goals, and deliverables. Develop detailed project plans, timelines, and resource allocations.

- Provide technical guidance and mentorship to the project team, ensuring best practices in data engineering, analytics, and AI/ML model development.

- Serve as the primary contact for client communications, ensuring clear and effective information exchange.

- Conduct Q&A sessions with senior and execution-level bankers.

- Ensure quality control and standardization of deliverables. Perform data discovery feasibility, establish data lineage for audibility and implement data quality checks.

- Establish clear milestones and governance plans, track project progress, and ensure adherence to deadlines.

- Oversee the setup of Azure SQL databases, Azure DevOps environments, and PowerBI servers.

- Monitor and optimize data pipelines, ensuring data integrity and efficient processing.

- Guide the development of PowerBI dashboards, meeting client requirements and provide actionable insights.

- Translating business problems into data-driven solutions.

- Communicating findings and recommendations to non-technical stakeholders.

- Fine-tuning algorithms and models to improve their performance.

- Conducting feature engineering to enhance model accuracy.

- Implementing cross-validation and other techniques to prevent over fitting.

- Writing efficient and maintainable code in languages such as Python, R, or SQL.

- Developing and maintaining data pipelines and workflows.

- Monitoring the performance of deployed models to ensure that they continue to perform well over time.

- Updating models as new data becomes available or as business needs change.

- Troubleshooting and resolving issues related to data quality or model performance.

- Establish and enforce data governance policies and standards.

- Ensure data integrity, accuracy, and security across all data science activities.

- Promote a data-driven culture within the organization