Senior Data Scientist

🕒 May 19

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Supplier.io

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤝 B2B

☁️ SaaS

📋 Compliance

B2B • SaaS • Compliance

Supplier. io is a leading provider of solutions for supplier diversity and sustainability programs. With years of experience and industry-leading data, Supplier. io helps organizations implement and scale supplier diversity and responsible sourcing initiatives. They offer a comprehensive suite of software tools, including data enrichment, analytics, carbon analytics, and supplier registration, which enable organizations to track and report on their supplier diversity and sustainability metrics. Their extensive database of diverse and sustainable suppliers supports companies in meeting their ESG goals, expanding their supplier networks, and benchmarking against peers. Trusted by many Fortune 100 companies, Supplier. io provides the resources and insights needed to build world-class supplier diversity programs that deliver measurable impact.

📋 Description

• Design, build, and iterate on ML-based entity resolution systems that match, link, and deduplicate supplier records across disparate data sources to produce trusted golden records • Build, train, and refine NLP and ML models (e.g., XGBoost, search ranking models) for supplier matching, classification, and data enrichment, with a focus on improving accuracy and recall • Evaluate and integrate emerging approaches, including LLMs, into our entity resolution and data intelligence workflows • Own the full ML model lifecycle: feature engineering, training, evaluation, monitoring, feedback loops, and iterative tuning in partnership with data engineering and product teams • Translate model results into business impact and clearly communicate tradeoffs, performance metrics, and recommendations to non-technical stakeholders • Build and maintain data products end-to-end, operationalize them within production data pipelines, and ensure they deliver reliable, scalable results • Execute and influence a cohesive data strategy that aligns with company objectives and supports analytics, reporting, and downstream product use cases • Own complex data modeling initiatives, including dimensional and analytical models that support business intelligence and advanced analytics • Drive continuous improvement by optimizing data pipelines, query performance, reliability, observability, and cost efficiency • Partner with Infrastructure, Product, and Engineering teams to ensure data systems meet best practices, security standards, and business needs • Create and maintain comprehensive technical documentation, including architecture diagrams, data flow maps, runbooks, and operations procedures • Troubleshoot and resolve complex, cross-system data issues and incidents.

🎯 Requirements

• Bachelor’s degree in Data Science, Computer Science, Machine Learning, Statistics, Engineering, or a related field • 7+ years of progressive experience in data science and/or data engineering, with demonstrated ownership of ML-based systems in production environments • At least 2 years in a senior or lead capacity preferred • Hands-on experience building NLP and LLM-based models in Python for real-world data science applications • Strong understanding of ML model lifecycle considerations, including evaluation, monitoring, feedback loops, and iterative tuning in partnership with data engineering and product teams • Strong ability to translate model results into business impact and communicate tradeoffs to non-technical stakeholders • Direct experience building or significantly improving entity resolution or search ranking systems, including ML-based approaches to record matching, linking, and deduplication at scale • Proficiency with ML frameworks and tools such as XGBoost, scikit-learn, PyTorch, or TensorFlow, and familiarity with search technologies such as Lucene/Elasticsearch • Demonstrated ability to build and maintain data products end-to-end by operationalizing models within production data pipelines, not solely tuning them • Advanced proficiency with Python and SQL for both data science and data engineering workflows • Experience with Snowflake and cloud-native data platforms (Azure, AWS, GCP, or multi-cloud environments) • Familiarity with data modeling, ETL/ELT processes, and modern data warehousing principles • Experience working in an agile development environment and collaborating through ticketing systems such as Jira and Github • Ability to communicate technical concepts clearly to technical and non-technical teams and influence decision-making • Strong problem-solving skills with the ability to troubleshoot and resolve ambiguous, high-impact issues • A results-oriented mindset with a demonstrated history of driving process improvements and technical excellence • Ability to work independently while also serving as a trusted technical partner and mentor to others • Ability to take vague requirements and turn them into technical roadmaps.

🏖️ Benefits

• Professional development opportunities • Remote work options