Scientific Data Engineering Lead

Saicon; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Saicon seeks a Scientific Data Engineering Lead to partner with our scientific community and translate complex data needs into robust data structures. You will travel to our New York City HQ and operating companies to ensure data is captured at the source for AI platforms.

You will lead data architecture, pipeline engineering, and applied ML, focusing on governance, scalable models, and production-grade ETL/ELT and dashboards for business partners.

Qualifications

• 5+ years in data engineering, data architecture or applied ML in scientific or manufacturing contexts.
• Ability to translate abstract domain concepts into deployed data models.
• Experience driving standards across decentralized multi-location orgs.

Responsibilities

• Define data semantics, architecture and quality requirements with scientists and engineers.
• Implement data governance and quality standards at source ingestion (LIMS, MES, CoA).
• Choose data models (relational, NoSQL, graph) for scientific datasets.
• Build cloud-based ETL/ELT pipelines using medallion architecture.
• Curate legacy datasets for ML readiness and AI use cases.
• Develop dashboards and web apps for business partners.
• Deploy ML models end-to-end with DevOps/MLOps practices.

Skills

Python

SQL

IaC

GCP

Azure

Data modeling

Delta Lake

DBT

PySpark

Airflow

Git

CI/CD

Time series data

Communication

Education

Technical degree in Chemistry, Engineering, or Computer Science

Tools

Databricks

Delta Lake

Power BI

Job description

THE ROLE

We are seeking an exceptional Scientific Data Engineering Lead to join our digital R&D capability. In this role, you will partner directly with our scientific community to translate complex domain knowledge into robust, traceable data structures, ensuring high-quality data is captured at the source to power internal scientific data and AI platforms.
You will be a key collaborator with critical stakeholders across our R&D and Digital teams, requiring travel to our Headquarters in New York City and Operating Companies (OpCos), both domestically and internationally. You will act as the vital translator between physical laboratory/manufacturing realities and digital data ecosystems, evaluating architecture trade-offs to turn materials and chemicals data into scalable, strategic company assets.

RESPONSIBILITIES

This role blends data architecture, pipeline engineering, and applied ML; day-to-day focus is expected to weigh toward pipeline engineering and governance, with the remainder as ongoing but secondary responsibilities.

The Scientific Data Engineering Lead will:

• Partner with scientists and engineers across OpCos to define data semantics, architecture and quality requirements to support scientific and business workflows.
• Work with OpCos to define and implement data governance and quality standards at the source of ingestion (e.g., LIMS, MES, CoA, Historian, etc.).
• Select optimal data models (relational, NoSQL, graph) and schemas for scientific datasets.
• Build and optimize cloud-based ETL/ELT pipelines using medallion architecture.
• Help curate and augment legacy datasets for machine-readability and AI readiness.
• Perform exploratory data analysis to monitor health and identify anomalies.
• Develop prototype dashboards and web apps for business partner consumption.
• Deploy baseline ML models into production, owning the pipeline end-to-end using DevOps and MLOps best practices, in partnership with our platform and CloudOps team.

THE PERSON

Required Skills

• Proficiency in Python, SQL and IaC for data manipulation and modeling.
• Familiarity with the google cloud platform (GCP) and Azure Cloud and IaC tools and techniques.
• Expertise in diverse data modeling frameworks (e.g., graph, relational, object-oriented).
• Knowledge of materials, chemicals, or manufacturing data types.
• Experience with cloud data lake architectures and delta shares.
• Experience building and optimizing cloud-based ELT/ETL pipelines using medallion architecture (e.g., Databricks/Delta Lake or equivalent), with transformation logic in dbt or PySpark and orchestration via Airflow, Dagster, or a cloud-native scheduler.
• Foundational machine learning, Git version control, and CI/CD skills.
• Familiarity with time series data.
• Strategic thinker with strong cross-disciplinary communication skills across technical, IT/OT and scientific disciplines.
• Technical degree in Chemistry, Engineering, or Computer Science.

Required Experience

• 5+ years of experience in data engineering, data architecture, or applied ML, ideally within scientific, industrial, or manufacturing data environments.
• Demonstrated experience translating abstract domain-expert concepts (from scientists or engineers) into functional, deployed data models.
• Demonstrated experience in facilitating change and aligning technical standards across a decentralized or multi-location organization.

Preferred Qualifications

• Experience working directly at the intersection of operational R&D and enterprise digital transformation.
• An advanced degree in Engineering or UX/UI is highly preferred.
• Experience working directly at the intersection of operational R&D and enterprise digital transformation.
• Familiarity with data governance tools or automated data-quality validation frameworks at the point of ingestion.
• Prior expertise communicating data-architecture trade-offs directly to senior management and diverse operational teams.