Senior Data Scientist

🕒 Maio 19

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Supplier.io

SiteLinkedInTodas as Vagas

51 - 200 funcionários

🤝 B2B

☁️ SaaS

📋 Conformidade

B2B • SaaS • Compliance

Supplier. io é uma plataforma SaaS de inteligência e gestão de diversidade de fornecedores que ajuda empresas a rastrear, descobrir, avaliar e mensurar fornecimento com fornecedores pequenos, diversos e sustentáveis. Seu conjunto de produtos inclui enriquecimento de dados, análise de gastos e relatórios, análise de carbono para visibilidade de Escopo 3, descoberta de fornecedores (Supplier Explorer), registro de fornecedores, benchmarking, análise de impacto econômico e relatórios de gastos de Tier 2. A plataforma é utilizada por grandes corporações para melhorar a diversidade de fornecedores, o fornecimento responsável e sustentável, além de quantificar o impacto econômico e ambiental.

Descrição

• 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.

🎯 Requisitos

• 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.

🏖️ Benefícios

• Professional development opportunities • Remote work options