Data Scientist / Data Engineer (AI & Machine Learning)

About the Role

We are on a transformative journey to harness the power of artificial intelligence and machine learning for the social good sector. We're looking for a foundational member of our data team to architect the data models and intelligence layer that enable AI agents to automate business processes across our products. This is a greenfield opportunity to work with rich, diverse datasets from across our product ecosystem, building core data layers and intelligent systems from the ground up.

Your work will provide the critical foundation for autonomous decision-making and automated reporting - strengthening Neon One's technological foundation and empowering our customers to significantly increase their social impact through data-driven automation.

What You’ll Do

• Data Modeling & Architecture: Dive deep into large, disparate datasets from across our application platform to design relational, dimensional, and analytical data models. You will extend these models into Salesforce Data 360, mapping data objects to ensure optimal performance for downstream analytics and Agentforce AI agents.

• Infrastructure & Pipeline Integration: Partner closely with our Data Engineer and Salesforce architects to define data requirements, ensure pipeline integrity, design schemas, and operationalize data flows — including implementing zero-copy data federation between our cloud environment (Snowflake/AWS) and enterprise CRM systems.

• Model Deployment & MLOps: Design, build, train, and validate machine learning models, with an emphasis on packaging, deploying, and monitoring these models efficiently in production at scale.

• Translate Data into Production Artifacts: Transform complex model outputs into production-ready data products and structured data views. You will communicate architecture and data modeling decisions to both technical and non-technical audiences, including our executive team and product managers.

• Deliver Platform Value: Develop the underlying data layers, views, and infrastructure that power reports and dashboards, delivering insights at an aggregate level (industry trends) and on a per-customer basis.

What You'll Bring

• Experience: 4+ years of hands-on experience in a data science or data engineering role, with a proven track record of developing data models and deploying machine learning infrastructure in production.

• Data Programming: Demonstrated proficiency in Python (or similar languages) and associated libraries for heavy data manipulation, ETL/ELT processes, and system integration.

• Cloud ML Platforms & MLOps: Hands-on experience building, training, and deploying machine learning models using a major cloud ML platform; direct experience with AWS SageMaker and automated deployment workflows is highly preferred.

• SQL & Data Engineering Foundations: Advanced SQL proficiency for complex data manipulation and query optimization, paired with a deep understanding of data warehousing concepts, dimensional modeling (e.g., Kimball paradigms), schema design, and database design patterns suited for machine learning pipelines.

• Autonomous Mindset: Demonstrated ability to work as a highly autonomous self-starter, comfortable with data ambiguity and taking ownership of infrastructure projects from start to finish.

• Communication: Exceptional communication skills, with the ability to articulate complex infrastructure and data modeling concepts to diverse stakeholders effectively.

• Education: A Bachelor's degree in a technical field such as Computer Science, Software Engineering, Information Systems, or a related quantitative discipline.

Bonus Points

• A Master's or Ph.D. in Computer Science or a relevant technical field.

• Deep familiarity or direct experience architecting within the Snowflake data platform (including Streams, Tasks, or Snowpark).

• Experience with Salesforce Data Cloud (Data 360), Mulesoft, or architecting data structures specifically optimized for autonomous AI agents (e.g., Agentforce).

• Experience with Natural Language Processing (NLP) techniques or engineering pipelines for Large Language Models (LLMs) and Vector Databases.

• Familiarity with building data products, multi-tenant databases, or data isolation in a SaaS environment.

• Prior experience working with or a passion for the non-profit sector.