
Chief Manager
2 weeks ago
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
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