Machine Learning Engineer

Responsibilities

Design, build, test, and maintain machine learning pipeline architectures (70%)

• Produce high-quality, reusable code for data ingestion, validation, and processing pipelines
• Architect and implement end-to-end ML pipelines including training, retraining, and inference systems for schools using the SST
• Design and build APIs to easily access, integrate, and manage data from different sources
• Ensure data infrastructure is in compliance with data governance and security policies
• Create comprehensive documentation for data infrastructure and ML pipelines, tailored for both technical and non-technical stakeholders
• Advance internal analytics reporting and automation capabilities as needed

Provide direct data support to partners (15%)

• Manage initial data lifecycle processes for new school onboarding including ingestion, transfer, audit, and validation
• Collaborate with data platform partners on integration and data transfer pipelines
• Provide technical guidance to partners on how to share data formatted in alignment with our data model and with appropriate data governance measures
• Address partner concerns regarding data security and ensure their specific requirements are satisfied
• Support data science initiatives through processing, cleaning, and analyzing data as needed

Collaborate and contribute across DataKind (15%)

• Support other data team members through code reviews and knowledge sharing across products
• Collaborate with the Product, Engineering, and Research teams to ensure seamless integration and alignment of work
• Effectively communicate project status and manage expectations with internal teams and partner organizations
• Maintain accurate and current project information in project management tools like Asana

Skillset

Required

• Alignment with DataKind’s mission and values, including our commitment to anti-racism
• Experience working across lines of difference (culture, identity, and time zone)
• At least 3 years of professional work experience in developing and deploying a machine learning product at scale
• Foundational understanding of machine learning and statistical methods for predictive modeling
• Expert in Python
• Experience with cloud computing (GCP preferred)
• Experience with databases (SQL, Postgres, PySpark, and/or other data query languages)
• Experience with DataBricks or a similar data intelligence platform
• Experience with data warehousing, orchestration, integration, and ETL tools
• Experience with modern source code management and software repository systems (i.e. Git)
• Experience documenting and implementing RESTful APIs
• Proven track record of successfully managing full life-cycle machine learning implementation projects with multiple stakeholders
• Solid understanding of Software Engineering principles and best practices and the data science project life-cycle
• Comfort and skill in communicating highly technical information to semi- and non- technical audiences
• Self-motivated, results-driven, and persistent in the face of challenges

Preferred

• Experience integrating data from SaaS providers
• Experience in the nonprofit sector and/or in a small startup organization
• Experience in scaling machine learning products, handling data quality and volume
• Certifications in cloud computing
• Advanced experience in machine learning—confident in applying, tuning, and evaluating a wide variety of algorithms
• Experience with software development and/or web-dev work (frontends, dashboards, etc.)
• Track record of strong technical writing for a variety of audiences
• Proven track record of (internal or external) client service orientation