What Makes Us a Great Place to Work:
We are proud to be consistently recognized as one of the worlds best places to work, a champion of diversity and a model of social responsibility.
We are currently ranked the #1 consulting firm on Glassdoor’s Best Places to Work list, and we have maintained a spot in the top four on Glassdoors list for the last 12 years.
We believe that diversity, inclusion and collaboration is key to building extraordinary teams.
We hire people with exceptional talents, abilities and potential, then create an environment where you can become the best version of yourself and thrive both professionally and personally.
We are publicly recognized by external parties such as Fortune, Vault, Mogul, Working Mother, Glassdoor and the Human Rights Campaign for being a great place to work for diversity and inclusion, women, LGBTQ and parents.
Who Youll Work With:
Bain’s Global Engineering leads the firm’s software development efforts and defines engineering standards for Bain globally.
The team ships software solutions to address client and internal needs, ranging from iterative prototypes to enterprise-grade production software.
This team is part of the broader Vector constellation of digital delivery teams, including Advanced Analytics and Advanced Digital & Product Team (ADAPT).
You will solve cutting-edge problems for a variety of industries as a machine learning engineer.
As a member of a diverse engineering team, you will participate in the full engineering life cycle which includes designing, developing, optimizing, and deploying new machine learning solutions and infrastructure at the production scale of the world’s largest companies.
What Youll Do:
- Develop and deploy machine learning solutions to solve enterprise
– scale challenges for Bain’s clients - Develop and champion modern Machine Learning Engineering concepts to technical and business stakeholders
- Build large-scale machine learning solutions with high degree of automation
- Translate business requirements into technical requirements and implementation plans
- Participate and help lead the full software development life cycle including documentation, testing, and code review
- Become a trusted advisor to external clients and internal stakeholders in Bain
- Travel is required (~20%) (Post-Pandemic)
- Architect, design, develop, build, and release robust and scalable Machine Learning Engineering solutions (50%):
- Enable data and technology for data exploration, wrangling and machine learning
- Feature engineering: automation at scale for data cleanup, enrichment, transformation, and & persistence in a feature repository
- Model training pipelines including cross-validation, performance testing, baseline testing, champion-challenger testing, explain-ability metrics and monitoring of training signals
- Inference delivery using batch, real-time or near real-time techniques.
Integrating inference with complex end user experience systems
- Enabling model inference feedback and monitoring for accuracy, bias, drift, and performance
- Enable machine learning experimentation for data scientists with data and tools
- Enable production data science infrastructure and tooling (20%):
- Participate in the full software development life cycle including reviewing distributed system designs, writing documentation and unit/integration tests, and conducting code reviews
- Improve internal and client systems infrastructure including CI/CD, microservice frameworks, and cloud infrastructure needed to support ML and data engineering workloads
- Provide technical guidance to external clients and internal stakeholders in Bain (30%):
- Scope and develop machine learning initiatives
- Support developing work plans with insights on machine learning capability roadmap and feature prioritization
- Assess current machine learning capabilities and recommend maturity roadmap for use cases
- Advise on tools and technology for machine learning capabilities
About You:
We are looking for someone who has:
- Bachelor’s in Computer Science or a related technical field
- 5+ years of experience with data science, data engineering, machine learning, and scalable distributed systems
- 5+ years of experience with machine learning and data engineering technologies such as MLFlow, KubeFlow, Neptune, SageMaker, DataRobot, H2O, Jupyter, Apache Airflow and Spark
- 2+ years of experience with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, Spark ML and Keras
- 3+ years of experience working on public cloud environments (AWS, GCP, or Azure), and associated deep understanding of machine learning and software engineering capabilities
- Experience with Python, Java, Scala, or Go
- Strong interpersonal and communication skills, including the ability to explain and discuss technical concepts and methodologies with colleagues and clients from other disciplines
Ideal candidates will also have experience in:
- Advanced degree in Computer Science or related technical field
- Real-time steaming distributed data processing using Apache Flink, Storm, Amazon Kinesis, Kafka, Spark Streaming, or Apache Beam.
- Deployment best practices using CI/CD tools and infrastructure as code (Jenkins, Docker, Kubernetes, and Terraform)
- Agile development methodology
- Elements of the PyData ecosystem including Cython, Numpy, Numba, Pandas, and Dask
- Engineering distributed systems and database internals (including handling consensus, availability, distributed query processing etc.
- Deploying end-to-end logging solutions such as the EFK stack
- Grafana dashboards
- Experience with administering and managing Kubernetes clusters (EKS, GCP, or AKS) and Helm
About Us:
Bain & Company is a global consultancy that helps the world’s most ambitious change makers define the future.
Across 59 offices in 37 countries, we work alongside our clients as one team with a shared ambition to achieve extraordinary results, outperform the competition and redefine industries.
We complement our tailored, integrated expertise with a vibrant ecosystem of digital innovators to deliver better, faster and more enduring outcomes.
Our 10-year commitment to invest over $1 billion in pro bono services brings our talent, expertise and insight to organizations tackling today’s urgent challenges in education, racial equity and social justice, economic development and the environment.
Since our founding in 1973, we have measured our success by the success of our clients, and we proudly maintain the highest level of client advocacy in the industry.