The internet is a vast ocean with millions of options, and it is growing by the minute. Brands today try to help us make sense of this madness with personalized recommendations based on our behaviours and purchase histories. But human beings are more than just that. Our personalities, motivations, and desires play a vital role in almost every decision we make every day. How much time can I invest? Can I afford it? How will it make me feel? These are but a few of the many questions that run through our minds before, during, and after a purchase.
To create the truly personalized experiences of the future, it is essential to understand the person behind the purchase. By combining your expertise in machine learning, our knowledge of games and a profound understanding of the human mind, we are going to build a video game recommendation engine that will allow us to make recommendations that align with people's fundamental motivations every single time.
We are a global team of 170+ tech junkies who manage one of the world's largest living libraries of video game metadata. Through partnerships with some of the world's biggest technology companies, our data receives billions of impressions every month and now we want you to help take us to the next level.
- 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.
- Creating automated anomaly detection systems and constant tracking of the performance of the Recommendation Engine.
- 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.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Must be well-versed with deep learning frameworks such as TensorFlow or Keras. Experience working on Recommendation Systems is a big plus.
- Must be proficient in Python and other machine learning languages.
- Must possess the ability to approach a data science problem and find smart and relevant solutions.
- Deep understanding of data structures, data modeling, and software architecture.
- Ability to conduct a rigorous analysis of data and chart out clear takeaway(s) from Big Data sources.
- Must be proficient in Natural Language programming.
- Must have extensive knowledge of probability and statistical skills, model selectors, advanced regression techniques like AIC, BIC, HyperParameters, Content-based and Collaborative Filtering methods, etc.
- Python packages such as Numpy, Scipy, Pandas, Sklearn, Matplotlib, and Tensorflow/Keras.
- Experience with various messaging systems, such as Kafka or RabbitMQ will be considered a plus.
- Experience with Big Data ML toolkits, such as Mahout, SparkML, or H2O is preferred.
- Implementation including loading from disparate data sets, and preprocessing using Hive and Pig will add huge value.
- Must be able to assess the scope of Big Data solutions and deliver on them.
- Should possess the ability to design solutions independently based on high-level architecture.
- Should coordinate the technical communication between the survey vendor/partner and internal systems.
- Must be capable of maintaining the production systems (Kafka, Hadoop, Cassandra, Elasticsearch).
- Collaborate with other development and research teams. Experience with building stream-processing systems, using solutions such as Storm or SparkStreaming.
- Experience with NoSQL databases, such as HBase, Cassandra, and MongoDB.
- Experience with the integration of data from multiple sources.
- Setting up and Management of Hadoop cluster, with all included services.
- Deploying the model into production, detecting anomalies, fine-tuning HyperParameters, and cross-validating as needed. Estimating resources, GPU requirements, analyzing the cost of running a continuous recommendation engine, ensuring the stability of the model, performance, accuracy, etc.
- 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, Hive, Spark, Flink, etc. Experience using web services: Redshift, S3, 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.
- Good hold of data architectures and competitive coding challenges is preferred.
- Experience building data pipelines and knowledge of DevOps and application monitoring systems are preferred.
- 3+ years of experience in Spark, HBase, Hive, Sqoop, Oozie, Flume, Java, Pig, Python, etc.
Other qualities we look for:
Purposeful and Aligned: Must have the ability to set clear and tangible objectives that deliver against our strategy.
Consulting Advisory Services: Should be able to provide expert knowledge and recommendations that will contribute to the growth of the business.
Result Oriented Work Ethic: Should display strong thought leadership, holding all stakeholders accountable as and when required.
Anticipate the needs of the Industry: Must be able to predict or anticipate market needs for which Gameopedia can provide solutions.
Agile and Innovative: Must possess intellectual flexibility and strong lateral thinking abilities.
Coach: Must demonstrate the ability to coach, mentor, inspire and develop others while facilitating learning, growth, and engagement.
Intellectual curiosity: Must have an insatiable thirst for knowledge and be continuously motivated to evolve and iterate.
Experience and Education:
Experience: 5 to 10 years
Education: Masters/B.Tech/M.Tech in Engineering, Applied Maths, Statistics.
- Recruiter screening
- Technical Telephonic Round
- Assignment/Final Technical Round
- HR Round
- Competitive salary
- Health insurance
- Casual dress code
- Collaboration friendly office