Chief Manager

Job DescriptionRole & 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 (ref:hirist.tech)