Would you like to work for one of the largest management consulting firms in the world? When a leading business need results this is where they come for solutions. They are able to advise clients on everything from strategy, operations to mergers and acquisition.
This Group is working on cutting-edge problems working to solve statistical, machine learning, and data engineering challenges for some of the world’s largest Fortune 5 companies.
It’s an opportunity to work at a global company that recently earned one of the best places to work in Glassdoor’s Best Places to Work 2019 and regularly ranks at the top of numerous other lists based on employee satisfaction ratings.
They have built a strong reputation for recruiting the best talent, developing their skills and providing an inclusive culture where everyone can truly flourish. They also offer the ability to work in multiple locations remotely as the current team is spread among Bay Area, LA, and Chicago.
You will work on cutting-edge problems for a variety of different industries as a software engineer specializing in Machine Learning. As a member of a diverse engineering team, you will participate in the full engineering life cycle which includes designing, developing, optimizing, testing and deploying machine learning solutions to support experimentation and innovation, at the production scale of the world’s largest companies. This position is not a research position.
• Partner with Data Science, Data Engineering, and Infrastructure teams to develop and deploy production-quality code
• Develop and champion machine learning concepts technical audience and business stakeholders
• Implement new and innovative machine learning tools, algorithms, and techniques
• This position will be located in San Francisco, Palo Alto, Los Angeles, Boston, Dallas, Austin, or Seattle
• Travel is required (20%)
• Bachelor’s, Master’s Degree or PhD in a quantitative discipline such as Computer Science, Engineering, Physics, Econometrics, Statistics, or Information Sciences such as business analytics or informatics
• 4+ years of experience with data science, machine learning
• 2+ years of experience building and supporting highly scalable, reliable, available, resilient, distributed and parallel production-grade computing systems or machine learning solutions
• Preparing data for analytics, building predictive models, and driving innovation in the modelling process
• 2+ years of experience working on public cloud environments (AWS, GCP, or Azure)
• Expert in Python and SQL
• Proficient in one or more of R, Java, C++, Scala, and Go
• Strong computer science fundaments in data structures, algorithms, automated testing, object-oriented programming, performance complexity, and implications of computer architecture on software performance
• Machine learning frameworks and tools (e.g. Pandas, numpy, scikit-learn, mlr, caret, H2O, TensorFlow, MXNet, Pytorch, Caffe/Caffe2, CNTK, and MLlib)
• Data ingestion using one or more modern ETL compute and orchestration frameworks (e.g. Apache Airflow, Luigi, Spark, Apache Nifi, and Apache Beam)
• Version control and git workflows
• A strong foundational understanding of statistical concepts and algorithms including linear/logistic regression, random forest, boosting, NNs, etc.
• Relational and NoSQL databases
• Deployment best practices using CI/CD tools and infrastructure as code (Jenkins, Docker, Kubernetes, and Terraform)
• Strong interpersonal and communication skills, including the ability to explain and discuss mathematical and machine learning technicalities with colleagues and clients from other disciplines
• Agile development methodology
• Open-source distributed computing and database frameworks such as Apache Flink, Ignite, Presto, Apex, Cassandra, and HBase
• Data warehousing and analytical database technologies such as Vertica, CitusDB, MapD, and Kinetica
• Engineering distributed systems and database internals (including handling consensus, availability, and distributed query processing)
• Deep learning frameworks such as Chainer, Theano, and Deeplearning4J
• GPU programming experience with CUDA
• JVM numerical libraries such as ND4S, Breeze, JBlas, and MLlib
• Elements of the PyData ecosystem including Cython, Numba, Dask, Spacy, and Gensim