We are a global team of 60 gaming junkies who are managing one of the worlds largest living libraries of video game data. From detailed game breakdowns to video trailers, our database has information on over 95,000 games across 160 platforms and were growing every day. We are hoping to find passionate and like-minded professionals who will help us innovate the industry and continue our commitment to providing our big business clientele with the most current, accurate, and detailed gaming data around.
Where You Fit In:
Calling all AI experts! Gameopedia is on the hunt for a data scientist to join us in our effort to create a revolutionary new video game recommendation engine that leverages our extensive metadata. You will be required to create and use advanced machine learning algorithms, perform advanced analytical and statistical modeling and much more to solve complex problems. You will work closely with other data scientists and data analysts to deliver intelligent analytical solutions to improve and innovate our data quality and ranking solutions.
- Selecting features and building recommendation engines using machine learning techniques.
- Data pre-processing using state-of-the-art methods.
- Enriching the company's data from third-party sources when required.
- Enhancing data collection procedures to include information that is relevant for building analytical systems.
- Develop custom data models and algorithms to apply to data sets.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Coordinate with various devs and businesses to implement models and monitor outcomes.
- Work with Sr. Data Scientists and keep improving and developing new skills.
- Must have experience with working on Recommendation Systems.
- Must be well-versed with deep learning frameworks such as TensorFlow or Keras.
- Must be proficient in Python and other machine learning languages.
- Must be able to approach a data science problem and find smart and relevant solutions.
- Understanding of data structures, data modeling, and software architecture.
- Ability to conduct rigorous analysis of data and chart out clear take away(s) from Big Data sources.
- Must be well-versed in EDA, Model Selection, Feature Engineering concepts.
- Must have extensive knowledge of probability and statistical skills, model, advanced regression techniques like AIC, BIC, Content based and Collaborative Filtering methods, etc.
- Python packages such as Numpy, Scipy, Pandas, Sklearn, Matplotlib and Tensorflow/Keras.
- Must be proficient with using query languages such as MongoDB and SQL.
- Hands on experience with Machine Learning/Deep Learning algorithms such as Linear Regressions, Logistic Regression, Decision Tree, SVM, Random Forests, Bagging and Boosting, Recommendation systems, ANN, CNN & RNN, etc.
- Experience with distributed data/computing tools: MapReduce, Hadoop, etc.
- Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, etc.
- A Kaggle profile with a list of relevant projects is preferred.
- Experience working with data architectures and data pipeline.
Other qualities we look for:
Consulting Advisory Services: Should be eager to provide expert knowledge and recommendations that will contribute to the growth of the business.
Result Oriented Work Ethic: Should display strong ownership of the work and product, holding all stakeholders accountable as and when required.
Anticipate The Needs of the Industry: Must be able to predict or anticipate business issues or problems for which Gameopedia can provide solutions, and identify and evaluate their feasibility.
Agile and innovative: Must possess intellectual flexibility and strong lateral thinking abilities.
Intellectual curiosity: Must have an insatiable thirst for knowledge and be continuously motivated to understand our system better.
Experience and Education:
Experience: 2 to 4 years
Education: Masters/B.Tech/M.Tech in Engineering, Applied Math, Statistics
- Competitive salary.
- Health insurance.
- Flexible working arrangements.
- Casual dress code.
- Collaboration friendly office.