Executives, CxO, C-Suite Folks, investors — everyone at the top wants to show that their company or project is on the cutting edge of the latest technological advances.

That’s the problem — countless executives see AI as a panacea for their business problems. As long as they invest in AI and the right experts, they can solve the problem quickly.

Unfortunately, it didn’t. Data science projects typically involve extensive experimentation, experimentation, error methods, and iterations of the same process before the final result is reached. This means it often takes months to achieve the desired results.

Data warehousing and ARTIFICIAL intelligence infrastructure require a lot of investment, depending on the size of the company, but finding insights in this work takes time because it takes time to generalize actionable insights from large amounts of data. So data scientists need flexible methods that give them the time and space to process data.

Many business leaders don’t want that. When data scientists eventually became frustrated with their leaders and unrealistic expectations, it led to the project’s demise.

How data scientists and business leaders can work together effectively:

· Enhance communication between data scientists and business teams. They must work together.

· Harness the business intuition and knowledge of business leaders, which can do wonders for data scientists.

· Co-develop measurable business performance matrices to measure data scientist performance progress.

· Agility is important for data scientists.

2. Big exposure of different data science projects on different platforms

Of the two options, do you prefer to:

· Option 1: A 9-to-5 job where you must adjust your skills and results to achieve the company’s goals, or

· Option 2: Highly flexible work life, can work from anywhere and achieve a high degree of personal growth.

Most people would probably prefer option 2. Who doesn’t like to work flexibly and have the freedom to do whatever they want?

Today, data scientists have a plethora of options:

· They can take their chances on platforms like Kaggle, Analytics Vidhya, and win exciting prizes and great reputations in the community

· Freelancers are in high demand in today’s companies with exciting short-term projects to offer

· Freelance data scientists get jobs through Spark, Hadoop, Hive, Pig, SQL, Neo4J, MySQL, Python, R, Scala, TensorFlow, NLP, computer vision, or other machine learning methods. Because they will engage with the problem and discover how to solve it.

· Many data scientists are enthusiastic about blogging and personal sections this season. Like Grant Sanderson — He’s my favorite!

Most of these are not available to resident data science professionals for obvious logistical and project-related reasons. To be honest, this is an inevitable cost of any project.

3. Ideal vs. reality — a huge gap!

This is a common problem in data science. There is a growing gap between what data scientists envision and what they actually do.

This is due to a number of factors, and it varies from data scientist to data scientist. Experience level is also part of this expectation difference.

Take amateur data scientists. They are often self-taught, acquiring knowledge from books and online resources. They don’t have much access to real projects or data sets. I meet a lot of data scientists who don’t know:

· How does machine learning workflow work?

· Where does writing software engineering fit into the data scientist skill set?

· What does it mean to put a model into production or deploy a model? , etc.

· What is the importance of data cleansing? Why should it take up most of the time?

As mentioned in the introduction above, the opportunity to use popular machine learning tools and state-of-the-art frameworks is very attractive for beginners. And frankly, it’s tempting for anyone else, too.

But here’s the thing — the industry doesn’t work like that. There are too many factors at work for a data science project to approach an online data science competition.

How to collect and store data; How to properly perform version control; How to deploy models into production – these are key aspects that companies expect scientists to master.

This expectation mismatch is the main contradiction that drives data scientists to quit. Novice and amateur data scientists are advised to communicate with their predecessors and company alumni to bridge the gap between ideal and reality.

4. Lack of skills upgrading among data science professionals

Who doesn’t like a challenge? The field of data science is ripe for these challenges because of the pace of progress. In the natural language processing (NLP) domain, for example, the number of development projects in the past two years has been mind-boggling.

Almost every data scientist is willing to work on these new technologies and frameworks. After all, who wants to build and iterate on the same Logistic model for years?

Data scientists are not immune to stagnation factors. After a certain point, you hit a wall, and the feeling of a new challenge is always close at hand.

In addition, there are two other factors related to managing expectations, mentioned above. Reality is always a mess, right? It’s inevitable that every employee will lack motivation after some point.

This is especially true of large, rigid companies. If you’ve worked at any blue chip company, chances are you’ve experienced this. Startups and mid-sized companies do this better, but they pose different challenges.

Here are three key reasons for employee turnover:

· Lack of infrastructure: This is true of most businesses, which lack computing systems, tool accessibility, and other infrastructure to support data scientists.

· Scope of business: The operating capacity of the business may be limited. It can be difficult for data scientists to extrapolate much insight from the data.

· Lack of RESEARCH and development: Data scientists like to explore areas outside of their job description. For example, if you’re a computer vision expert who wants to learn about NLP, the r&d area is the best place to do it. Most companies don’t, and that’s why they lose employees.

5. There is no clear standard for salary payment

Light up? Salary is one of the main reasons people want to go into data science and work full-time.

Data scientists are overpaid on average, according to reports from McKinsey, Glassdoor and others. Most people would be horrified by the numbers quoted in these reports.

The benchmark for data scientist salaries is sky-high. When top data scientists are poached by the likes of Google and Apple, you’ve no doubt read the news this year about Ian Goodfellow.

This happens all the time. Data scientists who are doing great work in their fields are often lured away by fortune 500 companies that offer ridiculously high salaries, while medium and small companies often don’t.

You need benchmarks in terms of salary. Even in mid-income companies, there is a clear line between the salaries of skilled novices and those of experienced data scientists.

· Even for high potential employees, there are unsatisfactory aspects of their work

· This is an important reason for employees to interact in the office and consider other job opportunities

It’s not so different from any other job, is it?

How do companies keep their star data scientists?

Here are some tried and true ways for companies to retain their most talented data scientists:

· Create a high-powered learning environment: This is essential for one’s personal and professional growth. In this field, there are new things to explore every day, and keeping up with this pace of development and providing a learning environment for data scientists to make incremental progress is crucial.

· Build a strong R&D team: Create r&d teams to carry out high-quality research in this area. Enabling people to research deep topics is the secret to excellence.

· Set a salary scale: Setting a salary scale builds trust and gives data scientists assurance that they are being paid according to the best industry standards. That’s understandable, but hard to do.

conclusion

Everything in data science is hyper-dynamic. We’re still coming to understand a lot of things, so it’s hard for an organization to just stay in one area, process, or structure.

As time goes on, there will be systems and processes in place and data scientists will have a satisfying working environment. It takes effort from both a business perspective and a data scientist perspective.

If you’ve ever been in the same situation, or would like to share your personal experience or anything else, feel free to leave a comment in the comments section!

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