Data Scientist

A Data Scientist is responsible for extracting valuable insights to inform decision-making. They utilize advanced statistical and machine learning knowledge, focusing on predictive and prescriptive analytics to solve complex problems and predict phenomena. They gather and analyze vast amounts of in-game data, such as player interactions, in-game economy metrics and user engagement patterns, transforming them into actionable insights for developers to make better decisions, improve game design, balance gameplay and retain players.

 

In multiplayer online games, Data Scientists play a crucial role in monitoring and adapting matchmaking algorithms, combating cheating and analyzing player feedback to ensure a fair and enjoyable gaming experience. They collaborate with Designers and Programmers to create data-driven features like personalized recommendations, dynamic difficulty adjustment and virtual economies. In single-player games, Data Scientists analyze player behavior, predicting future behavior and player success in completing the game, outcomes of various scenarios, segmenting players based on behavior, predicting challenging future sections for players, engaging in dynamic difficulty adjustment and other tasks. Data Scientists bridge the gap between data analysis and game design, using their skills to make video games more engaging, entertaining and profitable.

Tools

Autodesk 3ds Max

Unreal Engine

Unity

Maya

Substance Painter

Houdini

Blender

Adobe Photoshop

Marmoset Toolbag

Blender

COLLABORATION WITH OTHER TEAMS:

A Data Scientist closely collaborates with various other roles in the video game industry. One of the primary collaborations is with Designers and Programmers. A Data Scientist provides insights and recommendations based on player behavior and in-game metrics to help Game Designers make informed decisions about game mechanics, level design and overall gameplay experience. They also work with Programmers on implementing data-driven features.

 

Additionally, a Data Scientist collaborates with Product Managers and the Marketing team to optimize player engagement and monetization strategies. They provide data-driven insights for creating in-game events, promotions and virtual economies that enhance player retention and ultimately increase game revenue. Collaboration with Customer Support teams is also essential, as a Data Scientist can identify and address player issues through ticket analysis.

Skills

Depending on seniority, this position is expected to be able to master, do and deliver the following:

Hard skills

  • Data analysis and statistical expertise
    • Is skilled in statistical methods, hypothesis testing and regression analysis, extracting insights from complex datasets.
  • Programming and data manipulation
    • Utilizes code for automating data tasks, building models and developing algorithms for data analysis in games.
  • Machine learning and data modeling
    • Performs predictive analytics and develops personalized player experiences, creating predictive models.

Soft skills

  • Communication
    • Communicates complex data insights and recommendations clearly and concisely to non-technical individuals for easy understanding.
  • Problem-solving and critical thinking
    • Faces unique challenges and problems by devising innovative solutions and identifying opportunities for improvement.
  • Teamwork
    • Collaborates effectively, listens to others and works with them to achieve common goals in a rapidly evolving industry.

Seniority

Depending on seniority, this position is expected to be able to master, do and deliver the following:

Junior

  • Efficiently prepares data for processing, including handling missing values, removing outliers and transforming data into the appropriate format.
  • Conducts basic statistical analyses, such as calculating descriptive statistics, performing t-tests and creating simple data visualizations.
  • Presents insights to non-technical individuals and actively participates in collaborative problem-solving, clearly communicating findings to others.
  • Has basic knowledge of programming languages such as Python or R and data analysis tools and libraries (e.g. pandas, NumPy, and scikit-learn).

Medior

  • Conducts advanced data analysis and modeling, including predictive analytics, clustering and time series analysis, providing insights into player behavior.
  • Has a strong understanding of machine learning techniques and the ability to develop and apply machine learning models to various tasks.
  • Plans and conducts A/B tests to assess the impact of changes in the game, new features, or marketing campaigns, designs experiments and analyzes results.
  • Assists in shaping the data strategy, often leads projects, mentors juniors and fosters data-driven initiatives within the company.

Senior

  • Leads strategic decision-making in data-driven game development, defining key performance indicators (KPIs).
  • Collaborates with various teams to ensure that data-driven insights and recommendations are effectively integrated into the decision-making process.
  • Participates in R&D initiatives, explores new data science techniques, tech and tools for innovative gaming solutions.
  • Provides mentorship and guidance to juniors and intermediates, fosters their professional development and helps them build their skills and expertise.
DATA SCIENTIST // DATA //
DATA SCIENTIST // DATA //

EDUCATION:

If you are interested in working with data in the gaming industry, an education in areas such as data science, statistics, computer science or business analytics would be valuable to you. Look for programs that offer courses in data analysis, machine learning, database management, and programming languages ​​commonly used with data (such as Python, R, SQL, or Scala). An understanding of statistical methods, data visualization techniques and predictive modeling will be essential to using data to make decisions and optimize the player experience.

 

In addition to formal education, gaining practical experience through internships or projects related to data analysis can provide you with valuable insights and skills. Look for opportunities to work with companies or independent developers, where you can apply data-driven approaches in game design, player behavior analysis, marketing strategies, or business operations. Networking with professionals in the community, attending relevant conferences or workshops, and following industry trends will also help you stay competitive and informed in the field. Demonstrating an excellent ability to extract actionable insights from data and effectively communicate findings will be key to your success in these roles.

Resources

Books

  • “Python for Data Analysis” – Wes McKinney
  • “Data Science for Business” – Foster Provost, Tom Fawcett
  • “The Art of Data Science” – Roger D. Peng i Elizabeth Matsui
  • “Practical Statistics for Data Scientists” – Andrew Bruce, Peter Bruce
  • “Game Data Science” – Magy Seif El-Nasr et al

Individuals

  • Andrew Ng (@AndrewYNg)
  • Dr. Kirk Borne (@KirkDBorne)
  • Cassie Kozyrkov (@quaesita)
  • Hilary Mason (@hmason)
  • DJ Patil (@dpatil)

What does the interview for this position look like?​

News

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DATA SCIENTIST // DATA //
DATA SCIENTIST // DATA //