Bain & Company Inc

Machine Learning Engineer

US-CA-San Francisco | US-CA-Palo Alto
Job ID
2017-2590
Posted Date
11/30/2017
Category
Research & Analysis
Type
Regular Full-Time
US-CA-San Francisco

Overview

About Bain & Co’s Advanced Analytics Group

 

As a top management consulting firm, Bain & Co helps the world’s top business leaders solve their toughest problems.  Our work spans technology, analytics, strategy, marketing, organization, operations and M&A, across all industries and geographies. We've worked with the majority of the Global 500, thousands of major regional and local organizations, hundreds of nonprofits, and private equity funds representing 75 percent of global equity capital. We are proud of our clients' track record, like the fact that our public clients have historically outperformed the stock market 4 to 1.

 

We have 55 offices in 36 countries around the world, and work seamlessly together as one team for our clients wherever they need us.  We take great pride in the traits of Bain's culture that we hold most dear: a focus on results for our clients and a desire to make our office the best workplace for the world's top talent.

 

Bain’s Advanced Analytics Group is a team of high-impact quantitative technology specialists who solve statistical, machine learning, and data engineering challenges that we encounter in our client engagements.  AAG team members hold advanced degrees in subjects ranging across statistics, mathematics, computer sciences and other quantitative disciplines, and have backgrounds in a variety of fields including data science, marketing analytics and academia.

Responsibilities

As a Machine Learning Engineer in Bain’s Advanced Analytics Group, you will:

  • Engineer machine learning pipelines, working alongside fellow AAG machine learning and data engineers
  • Collaborate closely with business consulting staff as part of multi-disciplinary teams, to deliver the digital transformation goals of our clients using machine learning techniques and technology
  • Solve challenging problems in varied fields such as personalization/customer data analytics, resource optimization/operations research, natural language processing, computer vision and multi-source fusion of machine sensor data.
  • Develop re-usable common frameworks, models and components that address repeatable machine learning tasks and problems/data sets in specific industries or business functions
  • Work in a modern agile devops environment to deliver well-planned, tested, documented and maintainable code, using JIRA, Git and CI/CD technologies
  • Take responsibility for ensuring that our code, models and pipelines are deployed successfully into operations, and troubleshooting issues that arise
  • Design solution architecture in collaboration with team leads, with a focus on AWS/Azure cloud solutions but also including integration/deployment into other environments (e.g. on-premise) where appropriate
  • Share learnings with your team-mates in AAG ML/data engineering about theoretical and technical developments in our rapidly evolving field, collaborate with the open source community, and work together to create a great working environment that attracts other great engineering colleagues
  • Act as an ambassador and coach towards engineering teams at our clients and partners to raise their capabilities and ensure that solutions are successfully deployed and maintained

This position will be located in San Francisco or Palo Alto, and some travel will be required.

Qualifications

Must have:

  • Bachelors’, Masters’ Degree or Ph.D. in Computer Science, Machine Learning, Mathematics, Statistics, Physics, or a related quantitative field
  • 5+ years of experience with data science and machine learning in a business environment without a Master’s Degree, or 3+ years’ experience with a Master’s Degree or PhD
  • Strong computer science fundamentals including knowledge of data structures and algorithms, computational complexity, and object oriented/functional programming design patterns
  • Strong software engineering skills including experience of a modern agile development workflow using Git, unit testing and CI/CD
  • Experience performing and explaining a variety of data science and machine learning techniques such as random forests, gradient boosting, support vector machines, neural networks, clustering, matrix factorization, topic modeling, sequence classification and Bayesian techniques (including probabilistic graphical models and MCMC)
  • Strong background in Linear Algebra, Statistics and Numerical Mathematics.
  • Strong proficiency in Python and the PyData ecosystem including Numpy, Pandas, Scikit-learn
  • Proficiency in SQL
  • Strong Linux systems skills, and experience using compute and data environments on AWS (or Microsoft Azure/Google Cloud)
  • Strong interpersonal and communication skills, including the ability to explain and discuss mathematical and machine learning technicalities with colleagues and clients from other discipline
  • Ability to work independently and juggle priorities to thrive in a fast paced environment, while also collaborating as part of a team in complex situations
  • Interest in learning about business applications, business models and economics of machine learning and data technologies – you will have the opportunity to work with world-leading practitioners in this area from some of the 

 

Bonus points for experience in:

  • Specific domain areas of client demand, including: customer/marketing data analytics (churn analysis, segmentation), time series forecasting, computer vision, natural language processing and dialog systems, recommender systems, and optimization/operations research
  • Distributed computing and batch/stream processing frameworks such as Apache Hadoop, Spark, Flink and Ignite; experience with distributed filesystems and database technologies (e.g. MPP/NoSQL/Presto, Drill, Impala and others), and/or understanding of underlying distributed systems and database design principles (consensus, availability, distributed query processing etc)
  • Modern data engineering/ETL frameworks such as Apache Airflow and NiFi
  • Deep learning frameworks such as Tensorflow, PyTorch, MXNet, Chainer, Theano and Deeplearning4J
  • GPU programming experience with CUDA and/or higher-level abstractions
  • Scala/Java data science solution implementation, with ND4S, Breeze, JBlas, Mahout, Weka and/or MLlib, or experience with functional languages such as Clojure, Erlang, and Haskell
  • R programming, statistical computing and knowledge of advanced statistical techniques
  • Experience with other elements of the PyData ecosystem including Cython, Numba, Dask, Blaze, PyMC/Stan and Gensim
  • Data visualization using Javascript (d3, NVD3 and others) or Python (Vega, Altair, Bokeh, Plotly, etc)
  • Implementation of modern devops CI/CD pipelines and “infrastructure as code” environments using container orchestration technologies (Kubernetes/Mesos)

Compensation and Benefits

  • We offer highly competitive compensation to attract leading candidates
  • We have a generous package of benefits including healthcare coverage, 401K contributions, gym/fitness benefits and many others
  • We have a major focus on skill development and training, across internal training and knowledge sharing, company reimbursement of self-learning resources, and in-person attendance at conferences and learning events, based on self-directed learning roadmaps agreed with team leadership

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