Ghostwriting can be a great career if you are a talented writer who is full of ideas and good at analysis and at capturing the style and tone of various publications and of clients whom you'll be working with. However there are some things you should be clear about before starting to work as a ghostwriter.

What is Ghostwriter?

I think most of us know what a Ghostwriter is. As defined by Wikipedia, it’s:

“A person who is hired to write literary or journalistic works, speeches, or other text that are officially credited to another person as the author.”

A writer who is paid to write articles, books, reports, stories, website material or other content that is officially credited to another person is called a ghostwriter. A ghostwriter writes a piece of work, such as non-fiction or fiction, but does not put their name on it. Political leaders, celebrities, website owners, and executives typically hire ghostwriters to draft or edit their written material.

If you want to make money telling other people’s stories on the sly, you’ll have to be a good listener, a good researcher and a good writer. If you can do all that, you could make a living from this type of freelance work.

What does a ghostwriter do?

Ghostwriters are paid to write for someone else and allow them to put their name on that. Ghostwriter is their secret weapon behind the scenes. There is a high demand for ghostwriters to provide a wide range of content, like blog posts, articles, non-fiction books, and even celebrity autobiographies. Websites perpetually want fresh and interesting content. Many of us dream of getting a bestselling book, but simply don’t have the writing skills. You will satisfy all these needs and get paid doing something you like.

How to become a Ghostwriter?

• Gain experience: Ghostwriting is an excellent source of income and a great career alternative for an accomplished writer, it's certainly not an entry-level position. If you haven’t been published yourself, it’s unlikely you’re able to write to a publishable standard for someone else. Seriously, how are you able to write a book for someone else, in their voice, when you’ve never written one yourself? Better yet, write two or three. Shoot for a minimum of 50,000 words each so you get a sense for writing something that long dealing with the nuances of voice, narrative, flow, etc.

This makes sense on two levels: firstly, you need to be able to market yourself as a successful and experienced writer to potential clients who will be paying you to write for them. Secondly, you’ll need the experience you’ve acquired so far to navigate through a ghostwriting project.

• Identify your niche: While it is great to experience with various things when starting out as a ghostwriter, most ghostwriters have a “niche”: a unique point. Are you working on lyrics for top bands? Do you write business books? Make sure to concentrate on one thing. while it’s tempting to want a broad ghostwriting portfolio, having a specialist field can make you an professional in that niche, and you're more likely to get better, high-quality ghostwriting work in that area.
• Ability to work on tight deadlines: As a ghostwriter, you have to gather a deep understanding of your client’s needs and expectations. At the beginning of a project, you’ll get brief descriptions of project, outlining complete guidelines, expectations, and deadlines. And deadlines you'll get, you can’t expect an entire publishing team to wait for you while you sit around waiting for inspiration to strike.

If you’re an experienced writer, you’ll already know how to establish a regular, hardworking rhythm that works for you. However, it’s necessary to keep in mind that a ghostwriting project isn't something that may be completed in your own time. Do you have enough self-discipline to visualize a project through over a long period of time? Some ghostwritten projects take a few years to write, and procrastination is all too simple when you think you have all that time to achieve it.

• Research and communication skills required: Ghostwriting isn't the simplest job in the world. It requires smart communication skills and extreme patience in some cases for you to be able to do the work. You’re required to be a listener and a researcher. This sounds simple, but if you happen to be working with an uncommunicative subject, then it will really feel like a bloodshed.

Ghostwriters not only write up the story but do the majority of the legwork. If it’s an autobiography, you’ll require a top-notch interview skills. For other non-fiction ghostwriting, you’ll most likely be researching away.

Quality research also provides you the opportunity to confirm the information that your client has given you. Doing this doesn’t mean that you just disbelieve whatever they’ve told you. You’re simply fact-checking and qualifying the information that you just have been given that is your job.

• Put your ego aside: Ghostwriting isn't about you. It’s about your client. It’s obvious that you need to be an excellent writer but you also need to be comfortable switching your writing style to encapsulate your client’s voice, even though you're thinking that yours is better.

As a ghostwriter, your name won’t appear on the cover and the most you'll hope for is a thank you in the acknowledgements section, so there’s no area to enjoy dreams of seeing your name in print. You would possibly want to shout about your achievements but remember that you’re being paid to stay quiet.

If you can persuade your ego to step aside to put in the diligence for someone else, you'll gain publishing success and build a solid, skilled reputation through your ghostwritten work.

Applying for a data scientist job can be an intimidating task as there can be many things to take care in an interview process — right from justifying the practical knowledge to showcasing the coding skills. While we have earlier discussed articles on how to crack data science interview and what are the things to keep in mind while appearing for an interview for data science-related roles. This article deals with some of the things that you might be doing wrong if ever you are rejected in a data science interview.

Here are five things you may have been doing wrong:

Not focusing on the job description: The definition of data science jobs is not always the same and may mean different roles and responsibilities for different companies. Some of the commonly required skills may be a PhD in statistics, Excel skills, machine learning generalist, Hadoop skills, Spark skills, among others. The job description largely varies for every company and it is important to thoroughly dig it and carefully look for specific skills, tools and languages. It is important to display the skills that the potential recruiter is looking for so that they can shortlist you easily.

No specific distinction of technical skills: The technical skills in data science and analytics industry is quite wide and not mentioning your strengths correctly might jeopardise your chances of cracking the interview. For instance, it might not be apt to just say machine learning skills as it might include a whole spectrum of things ranging from linear regression to neural networks. And these sub-areas might further require knowledge of specific tools and software such as Python, Keras, R or Pandas. It is always advisable to give specific skills that you master than describing generic skills as might confuse recruiters of the exact skills that you pose.

Incorrect information and rephrasing work experience: To suit the data science job roles, many a times candidates rephrase their previous work experiences such as in the IT or software domains to present it as data science job roles, which might disguise your abilities initially but expose the depth and understanding of the skills later. You might have included job description aligning in a way that suits data science job roles but you might not have a deeper experience in it, which may get noticeable by recruiters during a one-to-one interaction. Mentioning of incorrect or misleading facts may also lead to recruiters rejecting you. For instance, the resume may state achieved an accuracy of say 90% on the test run, but what are the baseline and state-of-the-art score for this dataset to claim these numbers?

No mention about the projects that you have worked on from the scratch: Many times the only projects that a candidate mention in a resume are the ones they have done on Kaggle. While Kaggle is a platform for a lot of researchers to explore avenues in data science, it also serves as a source of practice for people who aren’t a pro in data science field and are trying to make a transition, mentions a recruiter in one of the forums. There are different kinds of the audience at Kaggle such as those who are playing around with the dataset or getting to know how problem-solving in data science works like, without having actual experience in solving or creating a new data science problems. So, listing just Kaggle project might be good but not definitive of how good your data science skills are. Even if it a Kaggle project, it is better if it is done from scratch. Other than that, it is important to mention the projects that you have worked on. It gives recruiters a chance to understand the problems you faced and the way you approached the problem, thereby giving them a glance at your problem-solving abilities.

The resume is full of buzzwords and no concrete proof of your skills: While the resume may suit the job description, but there are chances that you are rejected if there are too many buzzwords in the resume and no concrete way to prove that you actually pose those skills. You may mention in the resume that you have had experience with Hadoop, Excel or certain areas, but if you have showcased it real-time on platforms such as GitHub, it convinces the potential employers of the skills you have. They can look through various projects you have been a part of and see how you have dealt with real data. Hiring managers like to see the time that a candidate has spent from start to finish. Having a portfolio gives recruiters just that. There may be fancy sounding terms in the resume, but if you don’t have a proof to showcase it, you might be rejected for a potential data science job role.