ABOUT BURNING GLASS:

Burning Glass Technologies is an analytics software company whose tools play a growing role in informing the global conversation on education and the workforce and in creating a job market that works for everyone. Powered by the world's largest and most sophisticated database of jobs and talent, we deliver real-time data and breakthrough planning tools that inform careers, define academic programs, and shape workforces.

Burning Glass' applications drive practical solutions and are used across the global job market: by educators in aligning programs with the market, by employers and recruiters in filling positions more effectively, and by policy makers in shaping strategic workforce decisions. At the same time, Burning Glass' data-driven applications for workers and students help them choose career goals and build the skills they need to get ahead.

Our Analytics team plays a critical role in delivering insights for our products and clients from hundreds of millions of job postings and resumes. The team is responsible for developing models for classifying, validating, and understanding the job market data in our software products; ensuring and maintaining data quality; and creating reports which analyze areas of the labor market that are critical to our clients and partners. Our team's technology stack includes Python and R for data science and analysis, and SQL Server, ElasticSearch, and MongoDB for managing and working with our large datasets. ABOUT THE POSITION:

We are seeking machine learning/data science interns in summer 2019 to work on a range of predictive analytics models by leveraging deep learning techniques using labor market data. You will be performing research and development of machine learning algorithms to expand product capabilities.

KEY RESPONSIBILITIES:

• Apply machine learning, collaborative filtering and deep learning methods on massive job market data sets in various application such as text classification, content tagging, semantic similarity etc.
• Perform end-to-end data analysis pipeline, including data gathering, data cleaning and standardization, problem framing and hypothesis generation, inference, and presenting results to multiple levels of decision makers.
• Interpret data, extract trends and identify insights or opportunities for product decision.
• Apply machine learning algorithms on rule based systems to improve scalability and performance metrics.

QUALIFICATIONS:

• A graduate-level student (PhD preferred) in computer science, data science, statistics or any related discipline.
• Practical experience in applying core Machine Learning methodologies: Regression, Classification, Clustering, Matrix Factorization, Predictive Analytics, Decision trees, Support Vector Machines, Neural Networks/ Deep Learning.
• Excellent understanding of deep learning techniques  like CNNs (convolutional neural network), RNNs (recurrent neural network) as well as attention mechanisms in deep learning.
• Experince in developing deep learning applications using platforms such as Keras, TensorFlow or Pytorch.
• Experience with structured and unstructured data including natural language processing techniques.
• Experience in working with large data sets and distributed computing tools a plus (Map/Reduce, Hadoop, Hive, Spark, etc.).
• Familiarity with NLP toolkits such as Scikit-learn, numpy, scipy, R, Weka, NLTK, Stanford CoreNLP.
• Capable of grasping analytical concepts and clearly understanding how analytical solutions can be incorporated into broader business solutions.
• Exceptional ability to communicate findings to technical and non-technical audiences.
• Highly proficient in Python and SQL.

Burning Glass Technologies is committed to provide equal treatment and opportunity to all individuals without regard to gender, gender identity or expression, marital status, age, race, religion, national origin, ethnicity, disability, sexual orientation, veteran or military status, or any other category protected under the law. Burning Glass Technologies is an equal opportunity employer; committed to a policy of equal access, and inclusion, and a safe workplace free from discrimination, harassment, and retaliation.