DATA SCIENTIST JOB DESCRIPTION

As a data science professional, you play a crucial role in the world of business and technology. Fundamentally, you are responsible for analysing and interpreting large amounts of data, uncovering patterns and insights that can inform decisions and drive growth. You’ll identify the analysis and data sets that will assist the most crucial business objectives and typically use data to predict what may occur in the future. 

Here are some of the key responsibilities;

You will be responsible for acquiring and organising data from various sources. This could include databases, surveys, social media and more. Working with colleagues is essential to see that the data is clean and accurate and stored in a way that makes it easy to access and work with.

Once you have the raw data, your next step is to analyse it. This might involve using statistical models, data mining and machine learning algorithms to uncover patterns and insights. You will also need to be able to communicate your findings to others both within and outside of your organisation. In addition, there will be a requirement to solve complex problems.

Data science is a collaborative field and you will need to work closely with other professionals, including business analysts, data analytics managers, software developers and product managers. Communicating effectively and being comfortable working in a team is a must. Senior stakeholders are likely to be interested in your findings and depending on the structure and size of the business, you may then need very strong communication skills to present back the data.

The world of data science is constantly changing, with new technologies and techniques emerging all the time. It helps if you are analytical and have the mindset of a lifelong learner, staying up to date with the latest developments and continuously improving your skills.

These are just a few of the duties and responsibilities you will have as a data science professional. Whether you are working in a large corporation, a startup or as a freelance consultant, this is a vital role.

As a data science professional, problem-solving skills must be diverse and your familiarity with a multitude of platforms and tools should be extensive. Below, you'll find a list of fundamental skills that are required to work in this profession:

Proficiency in at least one programming language, such as Python, R, or SQL is essential. The use of these languages will facilitate the cleaning, data source manipulation and analysis of data.

Key to any role in this field is the cleaning and manipulation of data that can present multiple challenges, including missing values, incorrect data collection, duplicates and inconsistencies. 

Creating visualisations that are compelling, concise and crystal-clear is of utmost importance. Using coding tools such as Matplotlib, ggplot and Tableau is where a data scientist can create engaging and informative visual representations of their findings.

A strong understanding of statistical methods is fundamental and the ability to use these methods to analyse data and make predictions is crucial to being successful.

Familiarity with artificial intelligence machine learning algorithms such as decision trees, random forests and neural networks is vital. Using these algorithms is how predictive models are built to automate and solve real-world problems.

Cloud computing platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure are frequently used by professionals to store and process large amounts of data. 

Big data tools like Apache Spark, Hadoop, and NoSQL databases are also used to process and analyse vast amounts of data in a distributed environment. 

Collaborating effectively with other professionals including software developers, business analysts and product managers is important, not least because findings can be shared and acted upon.

Having these skills and knowledge of these platforms will set you up for success. However, it's important to note that the field of data science is constantly evolving and data scientists must continue learning and developing their skills throughout their career.

As a data science professional, you have a variety of career paths to choose from, however, your journey will depend on your qualifications, skills, experience and interests. Below is a typical representation of how you could reach the top of your profession.

Junior Data Scientist is often the starting point for many data science professionals meaning you will often work on simple data analysis projects, clean and prepare large data sets and learn the basics of data science. In this role, it is often the case that valuable experience is gained with various data science tools and techniques such as Apache Spark, SAS or MATLAB.

As you gain knowledge, you may move into a more senior position as a data analyst. There is likely to be responsibility for analysing and interpreting data, creating reports and visualisations and communicating your findings to others.

Whilst you continue to develop your skills, you may advance to the role of data scientist. This would mean that you take on more complex projects, lead data analysis initiatives and work on developing new algorithms and models.

At the senior level, you are responsible for shaping the direction of your organisation's data science initiatives. You may also mentor junior data scientists, collaborate with other teams and represent your organisation at industry events.

For those interested in management, you may choose to move into a leadership role as a manager of data science. This entails leading a team of data scientists, overseeing projects and developing strategy for your organisation's data science initiatives.

At the highest level, you may become a director of data science, responsible for overseeing the entire data science function of your organisation. This is the most important position in the profession that allows you to play a strategic impact on the organisation.

No matter which path you choose, a career in data science can be rewarding, challenging and exciting.

There are several steps you can take to increase your chances of getting a job in this exciting field:

Although careers within data science are possible without a degree, it is very rare, so consider obtaining a degree in a relevant field, such as statistics, computer science or mathematics. This will give you a solid foundation in the theories and concepts of data science.

Develop your skills in programming languages such as Python or R, data wrangling, data visualisation and machine learning. Consider taking online courses or attending workshops to further your knowledge. Understanding the tools you can use to help you process machine learning algorithms such as BigML will be needed.

Work on real-life projects and document your process, findings and results. This will give you the opportunity to showcase your skills and demonstrate your ability to solve real world problems.

Meeting people from the industry can be a game changer. Attending industry events and conferences and connecting with others in the field via online communities such as LinkedIn, Slack and GitHub can open up opportunities you may not have heard about otherwise. It will also help you stay up to date with the latest developments in the field and make connections with potential employers.

Search for job opportunities that align with your skills and interests and tailor your CV to each position you apply for. Consider reaching out to people in your network who work in data science or in related fields and ask if they know of any job opportunities or work with a specialist recruiter who understands the market and will be able to advise you well. When preparing for the interview, research the company and its data science initiatives and prepare answers to common interview questions. Be ready to discuss your previous projects, your technical skills and your experience with data science tools and techniques.

Keep learning because the field of data science is constantly evolving, consider taking on new projects, attending workshops and conferences to stay up to date with the latest trends and technologies in the field.

By taking these steps and being proactive in your job search, you can increase your chances of landing a job as a data science professional. Good luck!