Staff/Senior Data Scientist (Entity Resolution Team)

Join Enigma, a data-driven company where data science is at the core of our product. As a Staff/Senior Data Scientist on the Entity Resolution Team, you will work in a cross-functional team and own problems end to end, from raw source data to the customer interface. Your impact will be measured by the contributions you make to improve the accuracy of our data and the value of our product to our customers. You will have the opportunity to tackle exciting data problems, work directly with customers, and learn about data science and the world.

• Possess a strong understanding of entity resolution and data science principles, and apply this knowledge to solve complex problems.
• Lead the development and implementation of scalable data pipelines and models, ensuring high-quality and reproducible results.
• Collaborate with cross-functional teams to translate customer goals into working solutions, and actively contribute to improving the accuracy of data and the value of the product.

• Effective communication skills with both technical and non-technical audiences
• Principled, metrics-driven approach to solving complex data problems
• Self-driven to solve data problems to completion; fast learner and able to deliver results quickly
• Experience as a tech lead or as a mentor to other data scientists
• 6-8+ years of industry experience in statistics, experiment design, and distributed machine learning, with a track record of using it creatively to deliver customer impact
• Strong opinions and the ability to defend them, while being open-minded to other perspectives and approaches
• Advanced degree in a quantitative discipline (e.g., Computer Science, Mathematics, Physics) or equivalent industry experience
• Ability to disagree openly, commit completely, and maintain team momentum
• Experience building data products in the entity resolution, sales, marketing, KYB, or credit underwriting space
• Prior experience in Python, Spark, and Databricks
• Prior experience working with large and complex data pipelines