Machine Learning Engineer

  • Competitive
  • New York, NY, USA
  • Permanent, Full time
  • S&P Global
  • 17 Apr 19

Machine Learning Engineer

JobDescription :
The Team
The Data science team is a newly formed applied research & software engineering team within S&P Global Ratings that will be responsible for building and executing a bold vision around using Machine Learning, Natural Language Processing, Data Science, knowledge engineering, and human computer interfaces for augmenting various business processes.

The Impact
This role will have a significant impact on the success of our data science projects, delivering the highest quality software and data engineered solutions, ultimately enabling and augmenting our business processes and products with AI and Data Science capabilities.

What's in it for you
This is a high visibility team with an opportunity to make a very meaningful impact on the future direction of the company. You will work with senior members of the team to help define, design and build the systems. You will work closely with other senior technical staff across the company to create state of the art user interfaces/experiences and engineering solutions that will integrate with Augmented Intelligence, Data Science and Machine Learning based back-end services.

Responsibilities
As a Machine Learning (ML) Engineer you will be responsible for building user interface, server-side components, and their integrations with machine learning pipelines and their underlying data sources such as cloud based back-end services and databases. You will need to rapidly prototype data visualization components and plug them into machine learning pipelines, output of ML models and back-end services. You will also work with data scientists to test the quality of software components developed by using appropriate testing and validation processes, and iteratively enhance the system to be robust and scalable.

Basic Qualifications
BS in Computer Science or Engineering with 5-8 years of relevant industry experience.

Preferred Qualifications

  • Experience programming in a high-level language (e.g. Java, Scala, Python)
  • Experience with JavaScript and web application development frameworks such as Spring, Angular or React
  • Experience with distributed computing platforms, such as Hadoop (Hive, HBase, Pig) and Spark
  • Understanding of RESTful APIs, and integration experience with databases (SQL and NoSQL, Oracle, MySQL, Cassandra, MongoDB)
  • Experience with web application servers such as Apache, Tomcat, WebLogic and associated frameworks such as Spring, Spring MVC and ORM
  • Experience writing unit and functional tests and using testing frameworks
  • Experience working with cloud based managed services such as Amazon EMR, RDS, and S3
  • Comfortable with Linux, dependency management, build, CI/CD and DevOps tools
  • Integration experience with information retrieval and search engines, e.g. Solr/Lucene, Elastic Search
  • Knowledge of machine learning, natural language processing (NLP) and data mining techniques


To all recruitment agencies: S&P Global does not accept unsolicited agency resumes. Please do not forward such resumes to any S&P Global employee, office location or website. S&P Global will not be responsible for any fees related to such resumes.
S&P Global is an equal opportunity employer committed to making all employment decisions without regard to race/ethnicity, gender, pregnancy, gender identity or expression, color, creed, religion, national origin, age, disability, marital status (including domestic partnerships and civil unions), sexual orientation, military veteran status, unemployment status, or any other basis prohibited by federal, state or local law. Only electronic job submissions will be considered for employment.
If you need an accommodation during the application process due to a disability, please send an email to: EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person.
The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law.