Senior Data Scientist
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Job Description
Senior Data Scientist at Supplier.io specializing in ML and NLP for entity resolution and supplier intelligence. Driving data strategy and building scalable data solutions within a remote environment.
Responsibilities:
- 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
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