We are a team of ambitious, results-driven individuals with a proven track record of working with Fortune 500 industrial manufacturers, beauty brands, and chemical companies. We are a fast-growing company that hires talented, hardworking people who excel in high-performance environments and want to grow their careers quickly.
Design and build pipelines to collect supplier data and chemical product information (specifications, CAS numbers, certifications, SDS/regulatory documents, NAICS classification of manufacturing plants) from supplier sites, distributor catalogs, trade databases, and other public and semi-structured sources
Develop and maintain web scrapers and automated ETL workflows to keep supplier and product data current at scale
Clean, normalize, and reconcile inconsistent supplier data into structured, standardized formats suitable for internal tools and analytics
Apply chemical domain knowledge to validate and enrich data — resolving product names, CAS numbers, synonyms, and specifications across suppliers
Evaluate and improve matching and classification models to map suppliers and products to buyer requirements, and to identify overlapping or equivalent chemical offerings
Partner with Supplier Management and Engineering to define data quality standards, identify gaps in supplier coverage, and prioritize new data sources.
Own pipeline health and data quality, and drive the KPIs that measure overall data coverage
5+ years of experience in a data science, data engineering, or applied data role, ideally with exposure to messy, real-world or industrial datasets.
Working knowledge of chemistry or chemical industry data — comfort with CAS numbers, chemical properties, SDS documents, NAICS classification, and supplier certifications
Strong Python skills, with experience building web scrapers and data pipelines
Experience with data cleaning and normalization at scale, and a good eye for spotting inconsistencies in unstructured data
Familiarity with building or applying matching, deduplication, or classification models (traditional ML or LLM-based approaches)
Hands-on experience using AI tools and LLMs to accelerate data extraction, enrichment, or engineering workflows
Startup mindset with a strong sense of ownership — comfortable working independently in a fast-moving, remote environment with ambiguous, evolving priorities
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