What are the responsibilities of a data scientist?

The responsibilities of data scientist are to understand historical data, and present a correct picture of ‘what has currently happened in business?’ Once this is done, the analysis shifts to predicting the future i.e. ‘what could happen going forward’ and finally prescript prudent strategies to ensure that the future happenings are aligned to the priorities & targets of business.

Technically, this includes data cleansing & preparation for statistical analysis, application of statistical models on the prepared data, churning out insights & inferences from the data, and finally visualizing the findings in a readable format to business.
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A Data Scientist’s work involves understanding an association’s objectives and determine how data can be utilized to accomplish those objectives. A data scientist additionally plans prescient models which regularly incorporate machine learning and profound learning conventions; for the reasons for forecast, gleaning data, or data examination, and so forth

Skills: Programming skills (SAS, R, Python), Statistical and Mathematical Skills, Storytelling And Data Visualization, Hadoop, SQL, machine learning, Predictive Modeling

Data scientists work closely with corporate stakeholders to understand their goals and how data might assist them in achieving them. They create algorithms and prediction models to harvest data from the company, assess it, and share their findings with their peers. While every project is different, below is a general overview of the day today responsibilities:

  1. Collect information and locate data sources

  2. Create solutions and strategies that address business difficulties by analyzing a significant volume of organized and unstructured data.

  3. Design data strategy in collaboration with teammates and leaders.

  4. Discover trends and patterns by combining various algorithms and modules.

  5. Data visualization techniques and tools are used to present information.

  6. To develop novel data strategies, look into new technologies and techniques.

  7. Build data engineering pipelines and develop end-to-end analytical solutions from data collection to display.

  8. When needed, assist the data scientists, BI developers, and analysts with their projects.

  9. Working with the sales and pre-sales teams on cost reduction, effort estimation, and cost optimization.

  10. Using a combination of technologies, statistics, and machine learning, develop corporate analytics solutions.

  11. Lead conversations and evaluate the deployment viability of AI/ML solutions in business processes and outcomes.

  12. Architect, build, and monitor data pipelines and conduct knowledge-sharing sessions with peers for successful data use.