Are you a data scientist who is looking for real-world data science problems to sharpen your skills? Or maybe your organization has hard to solve data science problems while your data science team is busy with other projects. For either case, a data science competition platform can help.

Data science competitions help organizations solve complex business problems while enabling data scientists to learn from the experience and win awards. Organizations need to define the problem, provide data and put a prize on the challenge. Competing data scientists build and present different algorithms to be the winner.

What are data science competition platforms?

Data science competition platforms enable data science experts and enthusiasts to solve real-world problems, through challenges. These platforms serve as a solution marketplace for complex real-world data science problems.

The concept of platforms is simple. Businesses define their problem, provide the required data and offer awards in exchange for a solution. On the other hand,  crowdsourced data scientists apply for competition and provide the best possible solution.

How can businesses leverage these platforms?

These platforms provide win-win cases for businesses and data scientists. Data scientists even when they don’t win, learn from competing against others to solve real-life business challenges.

For businesses these competitions provide 4 benefits:

  1. Cost savings: Hiring a single data scientist is costly. As of 2020, the average annual salary of a full-time data scientist is $120,000 in the US. Thanks to data science competition platforms, companies can rely on the wisdom of the crowd to solve their organizations’ data science projects without employing numerous data scientists to solve their specific AI challenges. Since awards are less than data scientist salaries, companies have the opportunity to realize significant savings.
  2. High-performance solutions: Businesses choose the project that has the highest accuracy. In any other setting, they would need to settle with the only solution that they are presented or choose among 2 solutions presented by vendors. Competitions allow them to choose from numerous solutions, unlocking innovative approaches.
  3. Employer branding: These competitions help data scientists recognize the company’s brand and familiarize themselves with the companies’ challenges. This helps companies hiring efforts.
  4. Talent identification: Organizations can post example data science case studies in these platforms and examine the work of crowdsourced data scientists. If they like their expertise and approach on the topic, companies may hire them for upcoming projects.

Which problems are more suitable for competitions?

Data science competitions differ from standard data science projects. As its name refers, these platforms should provide competition and the winner gets a prize. Therefore ideal problems are

  • harder than standard data science projects: It takes a bit of effort to formulate the problem and prepare the data. It is not worth going through that effort for a challenge that will take an in-house data scientist a few days to solve. To maximize the benefits, host companies should launch competitions  that targets their most difficult problems.
  • without an existing solution: Most of the time, it is Once a problem is solved, it is all over the internet and anything gets old too fast. If the competition is about a new hot topic, competitors spend more time on the topic to perform extended research, customize algorithms, train advanced models, etc.
  • measurable in terms of relative performance: To crown a winner, the accuracy of the model should matter so that hosts can score each solution againts others.
  • important: Running a data science competition will take time and effort. Competitions should be run for problems where a performance improvement can bring benefits of >$10k per year.

What are the challenges of launching data science competitions?

Though there are challenges to launching data science competitions, competition organizers tackle almost all of these challenges for the company who wants to launch the competition.

Writing the problem statement

Data science competitions are more than just loading up data into a software package and running some algorithms. Data scientist competitors must understand the broader business problem to identify how to optimize the solution effectively.

De-identifying/encrypting data to be used in the competition

Since competition involves sharing data with competitors, data needs to be encrypted. While the encryption should not be breakable by the competitors,  the models built using the encrypted data should also function on the original data. There are numerous algorithms achieving such properties such as homomorphic algorithms and data science competition organizers are familiar with the selection of appropriate algorithms.

Attracting talented data scientists to the competition

While this can be a challenge for a company running its own data science competition, companies like Bitgrit that run data science competitions have access to large communities of data scientists. Bitgrit claims that there are 20k data scientists in its community which helps in creating enough demand for competitions.

What is the process of launching a competition?

In general, there are three information companies should provide to launch a competition:

Defining problem: Give a general summary of the challenge, what problem it should solve, and how it will be run. What is the significance of the task and the potential impact of the challenge?

Presenting data: Datasets can range from structured quantitative data to text, images and video. If necessary, the data science competition organizer would encrypt the data or support the company in encrypting its data.

Funding: The funds required to support the cost of hosting the competition and the prize pool for rewarding winners.

What are examples of data science competition platforms?


bitgrit is a platform that enables data scientists to interact with a global network and community. bitgrit’s competition platform aims to solve companies’ challenges efficiently using wisdom of the crowd. Other services bitgrit offers include AI consulting and data visualizations like their COVID tracker.


Kaggle offers both public and private data science competitions and on-demand consulting by a global data science and developer talent pool. Inside Kaggle, users can work in public API where they can reach 19,000 public datasets and 200,000 public notebooks to solve real-world problems across a diverse array of industries including pharmaceuticals, financial services, energy, information technology, and retail.

If you want to solve your data science problems with the help of consultants, feel free to read our data science consulting article.

If you want to take advantage of data science competitions to build low-cost, effective AI solutions, contact us:

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