Chemical Data Scientist

Job description

**About Valdera:** At Valdera, we empower innovators to turn ideas into reality by transforming how manufacturers source materials. We make it effortless for companies to find the best materials and suppliers for their needs, enabling them to build high-quality products at scale and deliver them to millions of consumers worldwide. 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. Our culture is built for exceptional individuals to take on meaningful challenges, collaborate with the top minds in our industry, and see the direct impact of their work. If you’re looking for a fast-paced environment where your ideas will drive real change, Valdera is the place for you. Join us, and let’s shape the future of manufacturing together. **Role Description:** We are hiring a C**hemical Data Scientist** to build and maintain the pipelines that keep Valdera's supplier and chemical product data accurate, current, and structured — the foundation every buyer and supplier relies on across Valdera's procurement platform. Data quality plays a critical role at Valdera. When a buyer launches a request, they expect to be matched with the right suppliers and accurate specs on the first try. Delivering that depends on clean, current data — CAS numbers, specifications, certifications, and regulatory documents pulled from thousands of inconsistent, often messy sources. This requires strong data engineering fundamentals and a working knowledge of chemical industry data. For example, you might take dozens of differently structured chemical supplier catalogs and turn them into one clean, standardized product database. You will take ownership of the full data pipeline — from scrapers and ETL workflows to data cleaning, matching, and classification models that connect suppliers to buyer requirements. You're energized by messy, real-world data and confident partnering with Supplier Management and Engineering to close coverage gaps. As a data-obsessed professional, you're dedicated to the accuracy our buyers and suppliers depend on. ### Role Responsibilities: - 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 ### Experience & Qualifications: - 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