create to do list according to step:
Step Into the Employer’s Shoes
Chess grandmasters step into their opponents’ shoes, trying to anticipate every move… Expert salespeople step into their prospects’ shoes, trying to understand their pains and desires… And we use this same tactic to write the perfect data scientist resume.

Here’s the thought experiment:

If you were the employer, what would you be looking for in a candidate?

Now, if the answer to that question sounds easy, you’re probably not thinking carefully enough. For example, “smart programmer with data and machine learning skills” is not detailed enough.

Instead, pretend you need to write a complete job description… How exactly would a data scientist help your business? Where would they fit in? Who would they work with/under? Which skills would you require? Which experiences would impress you?

Answering these questions will help you shape a coherent strategy right from the start.

To-Do List:

Carefully read the job descriptions for 5 positions you like.
Note the patterns. Which skills and experiences show up repeatedly?
Note the people. Will you work with the engineering team or with the analysts?
Write one or two paragraphs summarizing the perfect candidate in your own words.
Re-read this description each time you sit down to write or edit your resume! This will put you in the right frame of mind.

job position :
Key Indicators from the Employer's Perspective:

Master's degree in Mathematics, Statistics, or Computer Science / Information Technology or Industrial Engineering.
Microsoft Excel - Advanced
Python - Intermediate
Power BI - Intermediate
Job Description and Responsibilities:

Collecting and cleaning data from various sources to ensure its accuracy and validity.
Analyzing data to identify trends, patterns, and correlations within large datasets.
Developing and implementing statistical models and algorithms to extract actionable insights.
Visualizing and creating data dashboards to present reports and facilitate data-driven decision-making.
Continuously monitoring the quality and reliability of the data and identifying ways to improve the processes.
Additional Explanation:
Educational Background: The employer is looking for candidates with advanced education in fields like Mathematics, Statistics, Computer Science, IT, or Industrial Engineering. This suggests a strong focus on analytical and quantitative skills.

Technical Skills:

Microsoft Excel (Advanced): Proficiency in Excel for tasks like data manipulation, analysis, and complex formulas.
Python (Intermediate): The candidate is expected to have experience with Python for data processing, analysis, or implementing algorithms.
Power BI (Intermediate): Skills in Power BI for data visualization and creating dashboards.
Key Responsibilities:

Data Collection and Cleaning: A crucial step in ensuring data is accurate and ready for analysis.
Data Analysis: The candidate will work with large datasets to find trends and patterns that can help the business make informed decisions.
Statistical Modeling: Using statistical models to draw meaningful insights and predictions from the data.
Data Visualization and Reporting: Creating visual representations and dashboards to communicate findings to stakeholders.
Data Quality Monitoring: Ensuring that data remains reliable and finding ways to improve the process.