Work with stakeholders to understand their needs and translate them into technical requirements: This includes understanding the business goals of the stakeholders, and identifying the data that is needed to achieve those goals.
Develop and implement data science solutions: This includes using statistical and machine learning techniques to extract insights from data and develop models that can be used to predict future outcomes or prescribe actions.
Evaluate the performance of data science solutions: This includes tracking the accuracy of predictions and the effectiveness of recommendations.
Perform advanced data analysis and develop sophisticated predictive and prescriptive models: This includes using statistical and machine learning techniques to extract insights from data and develop models that can be used to predict future outcomes or prescribe actions.
Explore and preprocess data to ensure data quality and suitability for analysis: This includes cleaning, formatting, and transforming data to make it ready for analysis.
Develop and deploy machine learning models into production environments: This includes making sure that machine learning models are working properly and that they can be used to make predictions or recommendations in real time.
Mentor and guide data scientists and promote knowledge sharing: This includes sharing knowledge and expertise with other data scientists, and helping them develop their skills and knowledge.
Deliver measurable business value through data-driven insights and recommendations: This includes using data science to identify opportunities to improve the business, and developing and implementing solutions that can help the business achieve its goals.
Conducting data analysis
Developing data visualizations
Integrating data with other applications
Troubleshooting data problems
Stay abreast of the latest data science advancements and industry trends
Writing data documentation
Promote the use of data science within the organization
