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)
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Manager Data Science • Raipur
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