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QuantumBlack, AI by McKinsey.
St. Louis, MO

Principal Machine Learning Engineer - QuantumBlack - MLOps at QuantumBlack, AI by McKinsey. (St. Louis, MO)

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You will design, develop, and implement automated machine learning pipelines in a production environment using a combination of best-in-class OSS and vendor technologies.
Who You'll Work With
You will be part of our global data science and engineering community and you will lead ML productionization workstreams within cross-functional Agile project teams alongside Data Scientists, Machine Learning Engineers, other Engineers, Project Managers, Translators, and industry experts (i.e., SME).
You will work hand-in-hand with our clients, from data owners, users, and fellow engineers to C-level executives.
Who you are:
You are a highly collaborative engineer with keen problem-solving skills and you are passionate about building production-grade machine learning systems. You know how to engineer beautiful Python code and enjoy hands-on technical work.
You'll participate in the technical design, development, and implementation of automated machine learning pipelines in a production environment using a combination of best-in-class OSS and vendor technologies. Working within an Agile environment, you'll serve as a technical lead, providing input into machine learning architectural design decisions, developing and reviewing model and application code, and ensuring high availability and performance of our machine learning systems. Your efforts will be critical to successfully set up LiveOps protocols.
What You'll Do
  • Productionize and deploy ML models meeting the high standards and service-level agreement expectations of production-grade Enterprise IT in collaboration with multi-disciplinary QuantumBlack teams
  • Leverage expertise in modern OSS, Enterprise and public cloud ML tooling and services to devise the best approach to optimize and harden production-grade ML models
  • Build production ML components such as monitoring pipelines for data drifts and other quality issues, perform pre-deployment model performance validation and monitor models in production
  • Meet the high bar for software engineering best practices applied to the data science and analytics space, such as promoting clean code practices, leveraging static analysis and other code quality testing and tooling in CI/CD pipelines
  • Refactor code for feature engineering, ML model training and scoring into reusable libraries, APIs and tools to standardize and accelerate the development-to-deployment lead time
  • Play an active role in discussions and workshops for the setup, deployment, and scaling of use case pilots. Work across analytics use case squads to enable and ensure adoption and continuous improvement
  • Collaborate with client business leaders from data owners and use case users to C-level executives to understand their needs and architect impactful analytics solutions
  • Lead and contribute to R&D projects, internal asset development and the QuantumBlack Engineering community

OUR TECH STACK
While we advocate for using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the Python Scientific Stack; AWS SageMaker, GCP Vertex AI, Azure ML for building scalable ML solutions using public cloud ML stack; Seldon, Kubeflow, MLFlow, Grafana, Prometheus for machine learning pipeline management and monitoring; SQL, Airflow, Databricks, our own open-source data pipelining framework called Kedro, Dask/RAPIDS; Django, GraphQL and ReactJS for horizontal product development; container technologies such as Docker and Kubernetes, CircleCI/Jenkins for CI/CD, cloud solutions such as AWS, GCP, Azure, Snowflake, as well as Terraform and Cloudformation for deployment, and many more!
However, we advocate using the right tech for the right task. Technology evolves and engineering is responsible for staying up to date with the latest technologies and ensuring we make the relevant changes where needed.
WHAT YOU'LL BENEFIT FROM:
  • Real-World Impact - No project is ever the same. We work with top-tier clients across multiple sectors, providing unique learning and development opportunities internationally.
  • Fusing Tech & Leadership - We work with the latest technologies and methodologies and offer first class learning programs at all levels.
  • Multidisciplinary Teamwork - Our teams include data scientists, engineers, project managers, UX and visual designers who work collaboratively to enhance performance.
  • Innovative Work Culture - Creativity, insight and passion come from being balanced. We cultivate a modern work environment through an emphasis on wellness, insightful talks and training sessions.
  • Striving for Diversity - With colleagues from over 40 nationalities, we recognize the benefits of working with people from all walks of life.
  • Continuous development and progression - We offer an extensive choice of training sessions, ranging from workshops to international conferences, tailored to your needs as well as a personal mentorship system. We have multiple career paths and geographic locations to evolve within the Firm.
  • Global community - you will learn from colleagues worldwide by connecting both internally and externally through our various hosted meet-ups.

Visit our Careers website to watch our video and read about our interview processes and benefits.
Qualifications
  • Bachelor's degree in computer science, engineering or mathematics
  • Demonstrated experience building several high-impact ML solutions which have achieved meaningful business impact
  • Experience setting up at least one contemporary MLOps environment (e.g., experiment tracking, model governance, packaging, deployment, feature store)
  • Ability to write modern, clean, maintainable, scalable and robust code in Python and preferably at least one other language (e.g., Scala, Java, C++, JavaScript, Bash). Comfort with automated testing (e.g., unit tests) is a must.
  • Applied knowledge of common machine learning algorithms, techniques (e.g., train/test split, cross validation, hyperparameter optimization) and evaluation metrics (e.g., accuracy, precision, recall, AUC)
  • Hands-on expertise with cloud platforms (e.g., AWS, Azure, GCP, Snowflake), Linux environments, distributed computing frameworks like pySpark, and using container-based development workflows automated with DevOps tooling
  • Advanced knowledge of SQL and familiarity working with at least one common RDBMS (mySQL, Postgres, SQL Server, Oracle)
  • Ability to scope concrete deliverables with clear milestones and realistic timeframes
  • Experience translating roadmaps to sprint goals while working in an agile and iterative team set-up to adjust to changes in scope and objectives
  • Passion for and track record of effectively leading and mentoring more junior colleagues through, such as collaborative code reviews, pair programming and technical problem solving
  • Extensive commercial client-facing or senior stakeholder management experience
  • Willingness to travel in a moderate amount
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Technology we use

  • Engineering
    • C++Languages
    • JavaLanguages
    • JavascriptLanguages
    • PythonLanguages
    • RLanguages
    • ScalaLanguages
    • SqlLanguages
    • jQueryLibraries
    • ReactLibraries
    • PandasLibraries
    • PySparkLibraries
    • DjangoFrameworks
    • HadoopFrameworks
    • JupyterFrameworks
    • Node.jsFrameworks
    • SparkFrameworks
    • TensorFlowFrameworks
    • TorchFrameworks
    • MS AzureFrameworks
    • HiveDatabases
    • Microsoft SQL ServerDatabases
    • MongoDBDatabases
    • SnowflakeDatabases
    • SQLiteDatabases
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What are QuantumBlack, AI by McKinsey. Perks + Benefits

Culture
Volunteer in local community
Friends outside of work
Open door policy
Team owned deliverables
Team based strategic planning
Group brainstorming sessions
Open office floor plan
Diversity
Dedicated Diversity/Inclusion Staff
Unconscious bias training
Hiring Practices that Promote Diversity
Health Insurance & Wellness Benefits
Flexible Spending Account (FSA)
Disability Insurance
Dental Benefits
Vision Benefits
Health Insurance Benefits
Life Insurance
Wellness Programs
Mental Health Benefits
Retirement & Stock Options Benefits
401(K)
Performance Bonus
Match charitable contributions
Child Care & Parental Leave Benefits
Child Care Benefits
Generous Parental Leave
Family Medical Leave
Adoption Assistance
Vacation & Time Off Benefits
Generous PTO
Paid Volunteer Time
Paid Holidays
Paid Sick Days
Perks & Discounts
Commuter Benefits
Company Outings
Stocked Kitchen
Happy Hours
Relocation Assistance
Professional Development Benefits
Job Training & Conferences
Tuition Reimbursement
Diversity Program
Lunch and learns
Cross functional training encouraged
Promote from within
Mentorship program
Time allotted for learning
Online course subscriptions available
Paid industry certifications

An Insider's view of QuantumBlack, AI by McKinsey.

How would you describe the company’s work-life balance?

Unique, awesome! Never had it this good. We establish team norms at the beginning of each engagement to tailor an extraordinary work/life balance, have more fulfilling and impactful workdays, and leave room for personal and family time day after day.

Ravi

Principal – Data Engineering, New York

What does your typical day look like?

The morning usually starts with check-in calls with the team. The afternoon includes problem-solving sessions with leadership to resolve roadblocks and align on next steps. Throughout the day, we have blocks of heads-down coding time to develop pipelines for data cleaning and transformation, feature engineering, etc.

Aisuluu

Senior Consultant – Data Engineering, Toronto

How does the company support your career growth?

Strong culture of encouraging people to proactively ask for and provide feedback. The company also emphasizes mentorship and apprenticeship which can provide opportunities and safe places for people to ask questions and get help if needed.

Tianfang

Data Engineering, Boston

What's the biggest problem your team is solving?

We are solving how to develop scalable machine learning tools that data scientists and engineers across our organization can use easily and quickly. We are doing this across many domains, including life sciences, GEM, banking, and more.

Sanjay

Junior Principal – Data Science, New York

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