Machine Learning Engineer

We are looking for a Machine Learning Engineer that will help us discover the information hidden in vast amounts of data. This person will collaborate on data engineering efforts to access, transform, model, and store data to be used across the company, dive into exploratory data analysis to find insights and drive strategy, and engineer predictive models that directly impact our products and strategy.

This position will work with our data, product, and leadership teams. Its primary focus will be in applying data mining techniques, building high-quality prediction systems integrated with our products, automating scoring using machine learning techniques, building recommendation systems, and applying models to real-time data streams. In this role, you will also have the opportunity to guide the overall machine learning and data science strategy at AXS, partnering with our newly formed Global Data team to evangelize data-driven decision making.

The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations.

Responsibilities

• Select features, build and optimize classifiers using machine learning techniques
• Perform data mining using state-of-the-art methods
• Enhance data collection procedures to include information that is relevant for building analytic systems
• Process, cleanse, and verify the integrity of data used for analysis
• Perform ad-hoc analysis and clearly communicate results
• Create automated anomaly detection systems and constant tracking of their performance
• Identify opportunities for extending company’s data with third-party sources of information when needed

• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
• 2+ years’ experience working with common data science toolkits, such as Scala, Python, Numpy, Pandas, SciKit-Learn or similar
• Proficiency in using query languages such as SQL, SparkSQL, etc.
• Experience in AWS ecosystem, preferably having worked with Redshift
• Experience with distributed data/computing tools such as Spark, Map/Reduce, Hadoop
• Good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Good programming and scripting skills in Java, Python, Scala or similar
• Experience with Streaming data technologies such as Kinesis and Kafka preferred
• Experience with data visualization tools such as Mode Analytics, D3.js, GGplot, Looker, etc. preferre
• Experience with or desire to explore Deep Learning using some of the latest technologies (TensorFlow, PyTorch, Keras)

Skills: Algorithms, Amazon Web Services (AWS), Analysis Skills, Apache Hadoop, Apache Spark, Communication Skills, Data Analysis, Data Collection, Data Mining, Data Modeling, Data Quality, Data Science, Data Sets, Data Visualization Tools, Database Programming Languages, Deep Learning, Distributed Computing, Ecosystems, Emerging Technology, Financial Analysis, Java, JavaScript, Leadership, Looker, Machine Learning, MapReduce, Performance Analysis, Predictive Modeling, Process Improvement, Product Strategy, Python Programming/Scripting Language, Regression Testing, SQL (Structured Query Language), Sales, Scala Programming Language, Scripting (Scripting Languages), Statistics, Streaming Technology, Testing

AXS connects fans with the artists and teams they love. Each year we sell millions of tickets for over 300 clients in four countries. Since our founding in 2011, we’ve consistently pushed the industry forward and improved experiences for fans, making it easier than ever to discover events, find the perfect seats, and enjoy unforgettable live entertainment, and we continue to be a leader in the industry today. Headquartered in Los Angeles, AXS employs more than 500 professionals in multiple locations worldwide, including Cleveland, Charlotte, Dallas, Denver, London, and Stockholm.