Welcome to the data science stackexchange !

You don't give enough information in the question, despite requests for further details, and as such all we can do is answer generally.

Generally speaking, YES, you need a solid understanding of probability and statistics in order to be an effective data scientist.

It seems that you are interested in applying for DS internships. These are usually very competitive and you are very likely to come up against applicants that have a strong background in maths, statistics and probability. I would be surprised if you were able to demonstrate abilities that can make up for the lack of this knowledge. Some of these skills are "soft" skills including:

• communication at an appropriate level with team members and stakeholders,
• exceptional understanding of the business(es) and business logic/processes that you will be working on/with (substantive domain experience/knowledge),

while others are technical skills, such as:

• exceptional understanding of the business(es) and business logic/processes that you will be working on/with (substantive domain experience/knowledge).
• an extensive and deep understanding of programming/coding using Python, including software development concepts such as object oriented design and SOLID principles,
• a extensive and deep understanding of (modern) databases and data warehousing,
• experience deploying and monitoring projects. For example a CI/CD pipeline using github pages that trigger a build when changes are pushed, along with integration with a monitoring tool such as DataDog,
• good source control skills, including how to handle conflicts and merging of pull requests

The above list might be regarded as overkill, and it would be for a typical applicant but the point is that without the maths skills, you have to demonstrate skills that make up for that missing knowledge, and one way to do that is to show fantastic technical skills that would be very unexpected for someone in your position. The following, somewhat simplistic and overused, graphic is a useful description of the required skills for data science.

Welcome to the data science stackexchange !

You don't give enough information in the question, despite requests for further details, and as such all we can do is answer generally.

Generally speaking, YES, you need a solid understanding of probability and statistics in order to be an effective data scientist.

It seems that you are interested in applying for DS internships. These are usually very competitive and you are very likely to come up against applicants that have a strong background in maths, statistics and probability. I would be surprised if you were able to demonstrate abilities that can make up for the lack of this knowledge. Some of these skills are "soft" skills including:

• communication at an appropriate level with team members and stakeholders,
• exceptional understanding of the business(es) and business logic/processes that you will be working on/with (substantive domain experience/knowledge),

while others are technical skills, such as:

• an extensive and deep understanding of programming/coding using Python, including software development concepts such as object oriented design and SOLID principles,
• a extensive and deep understanding of (modern) databases and data warehousing,
• experience deploying and monitoring projects. For example a CI/CD pipeline using github pages that trigger a build when changes are pushed, along with integration with a monitoring tool such as DataDog,
• good source control skills, including how to handle conflicts and merging of pull requests

The above list might be regarded as overkill, and it would be for a typical applicant but the point is that without the maths skills, you have to demonstrate skills that make up for that missing knowledge, and one way to do that is to show fantastic technical skills that would be very unexpected for someone in your position. The following, somewhat simplistic and overused, graphic is a useful description of the required skills for data science.

Welcome to the data science stackexchange !

You don't give enough information in the question, despite requests for further details, and as such all we can do is answer generally.

Generally speaking, YES, you need a solid understanding of probability and statistics in order to be an effective data scientist.

It seems that you are interested in applying for DS internships. These are usually very competitive and you are very likely to come up against applicants that have a strong background in maths, statistics and probability. I would be surprised if you were able to demonstrate abilities that can make up for the lack of this knowledge. Some of these skills are "soft" skills including:

• communicating at an appropriate level with team members and stakeholders,
• an extensive and deep understanding of programming/coding using Python, including software development concepts such as object oriented design and SOLID principles,
• a deep understanding of databases,
• experience deploying and monitoring projects. For example a CI/CD pipeline using github pages that trigger a build when changes are pushed.
• good source control skills, including how to handle conflicts/merging of pull requests
• exceptional understanding of the business(es) and business logic/processes that you will be working on/with (substantive domain experience/knowledge).

The above list might be regarded as overkill, and it would be for a typical applicant but the point is that without the maths skills, you have to demonstrate skills that make up for that missing knowledge, and one way to do that is to show fantastic technical skills that would be very unexpected for someone in your position. The following, somewhat simplistic and overused, graphic is a useful description of the required skills for data science.

Welcome to the data science stackexchange !

You don't give enough information in the question, despite requests for further details, and as such all we can do is answer generally.

Generally speaking, YES, you need a solid understanding of probability and statistics in order to be an effective data scientist.

It seems that you are interested in applying for DS internships. These are usually very competitive and you are very likely to come up against applicants that have a strong background in maths, statistics and probability. I would be surprised if you were able to demonstrate abilities that can make up for the lack of this knowledge. Some of these skills are "soft" skills including:

• communication at an appropriate level with team members and stakeholders,
• exceptional understanding of the business(es) and business logic/processes that you will be working on/with (substantive domain experience/knowledge),

while others are technical skills, such as:

• exceptional understanding of the business(es) and business logic/processes that you will be working on/with (substantive domain experience/knowledge).
• an extensive and deep understanding of programming/coding using Python, including software development concepts such as object oriented design and SOLID principles,
• a extensive and deep understanding of (modern) databases and data warehousing,
• experience deploying and monitoring projects. For example a CI/CD pipeline using github pages that trigger a build when changes are pushed, along with integration with a monitoring tool such as DataDog,
• good source control skills, including how to handle conflicts and merging of pull requests

The above list might be regarded as overkill, and it would be for a typical applicant but the point is that without the maths skills, you have to demonstrate skills that make up for that missing knowledge, and one way to do that is to show fantastic technical skills that would be very unexpected for someone in your position. The following, somewhat simplistic and overused, graphic is a useful description of the required skills for data science.