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Data Scientist Job Description: The Ultimate Guide

Data Scientist Job Description: The Ultimate Guide

Data Scientist Job Description

A data scientist is someone who makes conclusion out of data. He fetches data from different sources, analyses it in the best way possible and draws inferences from them. He may build specific AI tools for them to automate certain processes in a company. In this post we will go step by step through a Data Scientist Job Description and understand its contents in an insightful way.

Basically, the profession of data science can be summed up as –

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

After looking at a lot of data scientist job description of various data science jobs out there we have come to the conclusion that Data scientist duties typically involve around creating machine learning tools for a company or a client like recommendation systems or data analyser tools. In this article, we present some basic job responsibilities and qualifications so you could learn the missing skills according to the needs of the current market.

big data scientist job description, junior data scientist job description and a chief data scientist job description may vary in certain aspects as a big data scientist will require more Big Data skills like Hadoop and Spark whereas a chief data scientist might require more experience and some leadership skills as well.

Responsibilities of a Data Scientist

A typical data scientist job description has a separate section listing out the responsibilities that a candidate will undertake during the job

  • Data mining and analyzing data

Data mining is the process of discovering patterns in large datasets so as to be able to develop better strategies in businesses. Data mining is a prerequisite to data science because bad data or poor quality of data may lead to discrepancies or errors in data analysis.

  • Developing data models and algorithms best suited to a particular scenario

Data modeling is a crucial part of the data science pipeline. Also, considering the fact that it is a very rewarding process, it receives the maximum attention from data science learners. It allows them to use a suitable algorithm and build the desired model.

  • Assessing effectiveness of the data model

This involves making use of a number of available parameters to measure the effectiveness of the data model and employing number of techniques to improve the efficiency

  • Making use of different data gathering techniques

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

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  • Use modeling to optimise customer experience

It is known that not all data is perceived as equal for the purposes of customer experience optimisation. The data scientist should be able to use modeling to make sense of the vast variety of data available to optimise customer experience

  • Develop processes and tools to monitor model performance and accuracy

Measuring data quality using defined models is not easy. Metrics like timeliness, completeness, consistency, accuracy, etc are very hard to measure. Although there are a number of tools available to improve accuracy, a data scientist may be able to develop new tools or processes better suited to the scenario at hand.

  • Make right predictions and decisions

Finally, a data scientist may be able to interpret the data after proper organisation and representation. This is the most important step and helps businesses to draw conclusions and take decisions in the right 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. Data scientists need to be solving business problems and for that basic problem solving skills like math, etc are required. We would suggest not to be disheartened if you do not have all of them and apply nevertheless but always keep acquiring more of them and improving the ones you already have.

No matter what kind of role you’re interviewing for, you are expected to know your tools. This means a 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 as a data scientist. This can mean things like ensemble forests, k-nearest neighbors, clustering, etc. Although, some of these can be implemented by python or R libraries, understanding the basic concepts behind these makes it easier to understand which technique to use 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 on real world projects.

  • Knowledge of statistical techniques and concepts

Knowledge of statistics is a prerequisite for a data scientist. You should be familiar with concepts of regression, distributions, statistical tests, etc. This allows you to understand if and when each technique is a valid approach or not. This is especially important in data-driven companies.

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

You will require high proficiency in either R or Python as they are the bread and butter of data scientists and you may need to have some experience with coding in languages like C, C++, Javascript, etc. It is not just about writing code but 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 pick up any new language fast.

  • Knowledge of database querying

All companies, big or small, make vast use of databases. Companies rely on databases and 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 required throughout. All these steps cannot be performed on the local computer of a data scientist because of reasons like limited processing power, limited memory, etc. 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.

Apart from all the above mentioned technical skills, a good data scientist should possess strong communication skills so that he can clearly and fluently translate all the technical findings of his team to a non-technical team such as marketing or sales. He should be able to provide them with useful insights, in addition to understanding their own needs, in order to help the business grow.

These may seem like a lot but if you’re interested in becoming a good data scientist, planning to build a career in data science and have a strong passion and commitment for data and learning, you should not let anything bother you. Data Science is already the next big thing in the technological field and with these skills at hand, you’re ready to conquer it.

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