Director, Machine Learning Engineering
Company: Karkidi
Location: Boston
Posted on: May 2, 2024
Job Description:
WHAT YOU'LL DOAs a member of the growing Data Science and
Machine Learning (ML) Engineering team in Bain's Advanced Analytics
Group, you will:
- Develop, deploy and support industry-leading machine learning
solutions, aimed at solving client problems across industry
verticals and business functions
- Provide thought championing in state-of-the-art
machine-learning techniques
- Collaborate closely with and influence business consulting
staff and leaders as part of multi-disciplinary teams to assess
opportunities and develop data-driven solutions for Bain clients
across a variety of sectors
- Translate business objectives into data and analytics solutions
and, translate results into business insights using appropriate
data engineering and data science applications
- Partner closely with other engineering and product specialists
at Bain to support development of innovative analytics solutions
and products
- Transform existing prototype code into optimized scalable,
production-grade software
- Manage the development of re-usable frameworks, models and
components
- Drive best practices in machine learning engineering and
MLOps
- Develop relationships with external data and analytics
vendors
- Act as Professional Development Advisor to a team of 3-5
machine learning engineers
- Support AAG leadership in extending and growing our machine
learning, engineering and analytics capabilities
- Help develop Advanced Analytics intellectual property and
identify areas of new opportunity for data science and analytics
for Bain and its clients
- Travel is required (30%)ABOUT YOU
- Advanced Degree in a quantitative discipline such as Computer
Science, Engineering, Physics, Statistics, Applied Mathematics,
etc.
- 10+ years of software engineering, analytics development or
machine learning engineering experience
- 3+ years of experience managing data scientists and ML
engineers
- Strong understanding of fundamental computer science concepts,
software design best practices, software development lifecycle and
common machine learning design patterns
- Solid understanding of foundational machine learning concepts
and algorithms
- Broad experience deploying production-grade machine learning
solutions on-premise or in the cloud
- Expert knowledge of Python programming and machine learning
frameworks (Scikit-learn, TensorFlow, Keras, PyTorch, etc.)
- Experience implementing ML automation, MLOps (scalable
development to deployment of complex data science workflows) and
associated tools (e.g. MLflow, Kubeflow)
- Experience working in accordance with DevSecOps principles, and
familiarity with industry deployment best practices using CI/CD
tools and infrastructure as code (e.g., Docker, Kubernetes,
Terraform)
- Extensive experience in at least one cloud platform (e.g. AWS,
GCP, Azure) and associated machine learning services, e.g. Amazon
SageMaker, Azure ML, Databricks
- Familiarity with Agile software development practices
- Strong interpersonal and communication skills, including the
ability to explain and discuss machine learning concepts with
colleagues and clients
- Ability to collaborate with people at all levels and with
multi-office/region teams
- Ability to work without supervision and juggle priorities to
thrive in a fast-paced and ambiguous environment, while also
collaborating as part of a team in complex situationsADDITIONAL
SKILLS
- Proficiency with core techniques of linear algebra (as relevant
for implementation of ML models) and common optimization
algorithms
- Experience using distributed computing engines, e.g. Dask, Ray,
Spark
- Experience using big data technologies and distributed
computing engines, e.g. HDFS, Spark, Kafka, Cassandra, Solr,
Dask
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Keywords: Karkidi, Cambridge , Director, Machine Learning Engineering, Executive , Boston, Massachusetts
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