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Mid LevelFull-timePythonRustAWSGCPAzureMachine LearningData ScienceGitGoAI
Salary Range
$80,750 - $109,250 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are currently looking for a Data Scientist to join our Client Analysis team at Socure. In this role, you will leverage your experience developing algorithms and analyzing massive data sets to tell data stories while working with the go-to-market team to drive revenue generation.
Key Responsibilities
- →Analyze massive customer data sets to create actionable insights and explain model performance
- →Act as a Data Science advocate by understanding Socure’s solutions and customers’ risk challenges
- →Create data stories to showcase how Socure can help customers eliminate identity fraud and streamline their end-user experience
- →Provide thought leadership and be the customer’s voice for building the next generation of Socure’s core models and products
- →Create tools and automated workflows to improve analysis speed, accuracy, and reproducibility
- →Partner with GTM teams to derive creative analysis approaches and identify opportunities for upsell and product optimization
- →Build ML models to explore different approaches over established models in production
Requirements
- ✓Degree in a quantitative field or equivalent work experience (advanced degree preferred)
- ✓3+ years of experience in data science, data analysis, or analytics engineering
- ✓Extensive theoretical and practical understanding of state-of-the-art machine learning methods
- ✓2+ years of experience writing clean, performant code in Python and working with massive datasets
- ✓Experience querying relational databases with SQL
- ✓Experience with cloud tools and technologies in AWS, Azure, Databricks, or GCP
- ✓Ability to tell compelling stories with data through dashboarding and presentations
- ✓Ability to explain complex algorithms to non-technical audiences
- ✓Excellent verbal and written communication skills
- ✓Experience in identity verification and fraud prevention (strongly preferred)
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