Chief Manager - Data Science

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

(ref : hirist.tech)