Senior Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, and enhance machine learning-based entity resolution systems that match, link, and deduplicate supplier records from various data sources to create reliable golden records.

• Construct, train, and fine-tune natural language processing and machine learning models (e.g., XGBoost, search ranking models) aimed at supplier matching, classification, and data enrichment, focusing on accuracy and recall improvement.

• Assess and incorporate innovative approaches, including large language models (LLMs), into our entity resolution and data intelligence processes.

• Manage the entire machine learning model lifecycle: feature engineering, training, evaluation, monitoring, feedback loops, and iterative tuning in collaboration with data engineering and product teams.

• Convert model outcomes into business value and effectively convey trade-offs, performance metrics, and recommendations to non-technical audiences.

• Develop and sustain data products from start to finish, operationalizing them within production data pipelines to ensure they yield dependable, scalable results.

• Implement and shape a unified data strategy that aligns with organizational goals and supports analytics, reporting, and downstream product applications.

• Lead complex data modeling projects, including dimensional and analytical models that facilitate business intelligence and advanced analytics.

• Promote ongoing enhancement by optimizing data pipelines, query performance, reliability, observability, and cost-effectiveness.

• Collaborate with Infrastructure, Product, and Engineering teams to ensure data systems adhere to best practices, security protocols, and business requirements.

• Generate and maintain detailed technical documentation, including architecture diagrams, data flow charts, runbooks, and operational procedures.

• Diagnose and resolve intricate, cross-system data challenges and incidents.

⛳️ Requirements

• Bachelor's degree in Data Science, Computer Science, Machine Learning, Statistics, Engineering, or a related discipline.

• Over 7 years of progressive experience in data science and/or data engineering, showcasing ownership of machine learning-based systems in operational settings.

• A minimum of 2 years in a senior or leadership role is preferred.

• Practical experience in developing NLP and LLM-based models in Python for real-world data science applications.

• Robust understanding of machine learning model lifecycle aspects, including evaluation, monitoring, feedback loops, and iterative tuning in cooperation with data engineering and product teams.

• Strong capacity to translate model outcomes into business impact and articulate trade-offs to non-technical stakeholders.

• Direct experience in constructing or significantly enhancing entity resolution or search ranking systems, including machine learning-based methods for record matching, linking, and deduplication at scale.

• Proficient with machine learning frameworks and tools such as XGBoost, scikit-learn, PyTorch, or TensorFlow, and familiar with search technologies like Lucene/Elasticsearch.

• Proven ability to build and manage data products end-to-end by operationalizing models within production data pipelines, rather than just tuning them.

• Advanced proficiency in Python and SQL for both data science and data engineering tasks.

• Experience with Snowflake and cloud-native data platforms (Azure, AWS, GCP, or multi-cloud environments).

• Understanding of data modeling, ETL/ELT processes, and contemporary data warehousing principles.

• Experience in an agile development environment and collaboration through ticketing systems like Jira and GitHub.

• Ability to convey technical concepts clearly to both technical and non-technical teams and influence decision-making.

• Strong problem-solving skills with the capability to troubleshoot and resolve ambiguous, high-impact challenges.

• A results-driven mindset with a proven track record of promoting process improvements and technical excellence.

• Ability to work independently while also acting as a reliable technical partner and mentor to others.

• Capability to transform unclear requirements into actionable technical roadmaps.

🏝️ Benefits

• Opportunities for professional development.

• Options for remote work.