Data Scientist Job Description: The Ultimate Guide

by | Aug 6, 2018 | Data Analytics

5 Min Read. |

Data Scientist Job Description

A data scientist job encapsulates the conclusion of data after fetching it from different sources. Data Scientist job description can be summarized in the following two points:

⇒ Analyzing data in the best way possible and

⇒ Drawing inferences from them

His work may also include building of specific AI tools and automate certain processes in his workplace. Hence, in this post, we will go step by step through a Data Scientist Job Description and therefore understand its contents in an insightful way.

Basically, data science job description can be summed up in 5 simple steps –

  1. Ask a question
  2. Get the data
  3. Explore
  4. Model and
  5. Communicate

Data science jobs have conquered a huge success market and thus include the following job activities:  

⇒  The creation of machine learning tools for a company 

⇒ A client like recommendation systems and

⇒ Data analyzer tools

Hence, here we present some basic job responsibilities and qualifications of a data scientist to give you a clear idea of where to update your skills.

The job description may vary for a big data scientist, a junior data scientist job description and a chief data scientist. A big data scientist requires more Big Data skills like Hadoop and Spark and a chief data scientist requires in-field experience and leadership skills.

Responsibilities of a Data Scientist

Section listing of the responsibilities of a data scientist job are listed below:

  • Data mining and analyzing 

It is the process of discovering patterns in large datasets and enhancing the development of better strategies in businesses. Data Mining is a prerequisite to data science because poor quality of data may lead to errors in data analysis.

  • Developing best-suited data models and algorithms

Data modeling is a crucial part of the data science pipeline because this process receives the maximum attention from its learners. Therefore, it allows them to use a suitable algorithm and build the desired model.

  • Assessing the effectiveness of the data model

This process involves making use of a number of available parameters and measure the effectiveness of the data model. Data modelling employs a number of techniques to improve the efficiency.

  • Making use of different data-gathering techniques

There are a number of techniques used for data collection. Any kind of model needs reliable data and hence choosing the right technique makes all the difference. Therefore, the data scientist should have the correct insight about which technique to go for.

 

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  • Use modelling to optimize the customer experience

For the purpose of customer experience optimisation, all data is not perceived equal. Hence, data scientists should be able to use modelling and must know how to make sense with vast variety of available data. Therefore, he must optimise according to the customer experience.

  • Develop processes and tools to monitor model performance and accuracy

Measuring data quality using defined models is not easy because metrics like timeliness, completeness, consistency, and accuracy are very hard to measure. Although there are a number of tools available to improve accuracy but a data scientist may be able to develop new tools or processes.

  • Make right predictions and decisions

Finally, a data scientist may be able to interpret the data after proper organization and representation. This is the most important step and helps businesses to draw conclusions. This also upgrades the right- decision direction.

Qualifications to Become a Data Scientist

A data scientist job description usually also has a separate section listing out the qualifications required by candidates to apply.

  • Problem-solving skills

Data science isn’t just about memorizing algorithms and writing code. The professionals should possess basic problem-solving skills like maths and must keep on acquiring more.

No matter what kind of role you’re interviewing for, you are expected to know your tools. This includes statistical computer language like R or Python or a querying language like SQL.

  • Knowledge of machine learning techniques

Good understanding of machine learning methods is vital for a data scientist. These methods may include ensembling forests, k-nearest neighbors and clustering. Although, some of these can be implemented by python or R libraries. It has become easier to understand which technique to use and when. Both supervised and unsupervised machine learning algorithms should be on the fingertips of an aspirant. The mathematics and logic behind these algorithms should be studied thoroughly and applied to real-world projects.

  • Knowledge of statistical techniques and concepts

Statistics knowledge is a prerequisite for a data scientist. You should be familiar with concepts of regression, distributions, statistical tests, etc. This allows you to understand where and when each technique is valid. It’s especially important in data-driven companies.

  • Coding knowledge and experience with languages like C, C++, Javascript

Data Scientists require high proficiency in R or Python including some experience in coding in languages like C, C++, Javascript, etc. The tasks are not just limited to writing code but also about being comfortable with using different programming environments to analyze data. Any hesitation with coding could be a deal-breaker. A data scientist must be able to grab any new language.

  • Knowledge of database querying

All companies, big or small, make vast use of databases. Data scientists use SQL and other querying languages to extract the data they need from these databases. If you want to manage databases, you need to learn a querying language like SQL. Even though Hadoop or NoSQL have become a component of data science, it is expected of you to be able to write complex SQL queries. 

  • Experience with web services like Spark, DigitalOcean, etc

A data science process consists of a number of steps, with data being the main thing revised throughout. These steps cannot be performed on the local computer of a data scientist because of limited processing power and limited memory. Hence, a data scientist should know how to offload the computing work to a cloud-based virtual machine.

  • Excellent communication skills to coordinate between different teams.

 The communication skills of a good data scientist must be clear and fluent. Hence, he would be able to translate all the technical findings to non-technicals and hence ease the knowledge flow in his team. He should be able to provide them with useful insights and understanding so as to help grow business.

I’ve cleared up all the points regarding the data scientist job description. If you are passionate about data learning,  start with a plan to build a career in data science. Data Science is already the next big thing in the technological field. Therefore, to know how to become a Data Scientist, read this Quora answer.

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