Level

Socure

Senior Data Scientist - Document Verification

Socure is hiring a Senior Data Scientist - Document Verification in San Francisco, United States. It pays $191k-$230k a year and Level rates it ; you can apply on Socure.

Level's read on this job

  • Level rates this data scientist role Builds AI ●●●●: the job is building AI systems.
  • Pay on this posting starts 15% higher than the typical data scientist posting on Level.
  • The posting mentions SageMaker, Databricks and AI evaluation among its tools and skills.
  • Level classes it as a senior role.
  • Socure has 82 other open roles on Level.

AI in this role

Drive machine learning innovation and build production models for document verification and fraud detection.

sagemakerdatabricks
ai-evaluationcomputer-visionmachine-learningpythonmodel-evaluationfraud-detectionanalytics

Why Socure?

Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.

We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.

Senior Data Scientist – Document Verification

Summary

We are seeking a highly technical Senior Data Scientist to drive machine learning innovation for Socure's Document Verification (DocV) platform. This role combines model development, product analytics, model evaluation, and ML tooling to improve fraud detection, customer experience, and operational efficiency. You will partner closely with Product, Engineering, Fraud Operations, and Data Science to build production ML models, generate actionable insights, and develop scalable evaluation and automation frameworks.

Responsibilities

Machine Learning & Model Development

  • Design, develop, and improve ML models for document verification, fraud detection, image quality assessment, biometric verification, and related use cases.

  • Research new features, modeling approaches, and fraud detection techniques to improve production performance.

  • Partner with Engineering to deploy, monitor, and continuously improve production models.

Analytics & Model Evaluation

  • Analyze product performance, fraud trends, customer behavior, and emerging attack vectors.

  • Design and execute model evaluations using offline and production datasets, measuring precision, recall, FAR/FRR, and business impact.

  • Develop dashboards and KPIs to monitor model health, product performance, and operational metrics.

Tooling & Automation

  • Build tools and automation for model evaluation, performance monitoring, labeling workflows, and fraud investigations.

  • Develop self-service analytics and experimentation frameworks.

  • Improve analytics infrastructure supporting model development and monitoring.

Cross-functional Collaboration

  • Partner with Product, Engineering, Fraud Operations, and Solutions Engineering to translate business needs into data-driven solutions.

  • Communicate technical findings and influence product and modeling decisions.

Qualifications

Required

  • MS/PhD (or equivalent experience) in Computer Science, Statistics, Data Science, or a related field.

  • 5+ years of experience in machine learning, data science, fraud analytics, or product analytics.

  • Strong experience developing and evaluating production ML models.

  • Expert SQL and Python skills.

  • Experience with Databricks, Spark, AWS Sagemaker, or similar platforms.

  • Strong understanding of experimentation, statistical analysis, and ML evaluation.

Preferred

  • Experience with computer vision, deep learning, or transformer-based models.

  • Experience designing and analyzing A/B tests, online experiments, and statistical evaluations of product or machine learning performance.

  • Experience defining product KPIs and building dashboards to monitor product performance, customer behavior, and operational metrics.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.



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How we rate this

Senior Data Scientist - Document Verification at Socure rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI EvaluationComputer visionMachine learningPythonModel EvaluationFraud DetectionAnalyticsSageMaker

Also common in data scientist jobs at this level

Pay

This job starts at
$191k
Typical data scientist job
$166k

This posting starts 15% above the typical data scientist job. See data scientist jobs

Questions you could be asked

  1. How do you decide that one model's output is better than another's for a given task?
  2. Walk me through a computer vision problem you solved, from raw data to a deployed model.
  3. Tell me about a project where machine learning was part of your work. What did you do?
  4. Tell me about a project where python was part of your work. What did you do?
  5. Tell me about a project where model evaluation was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI Evaluation, Computer vision, Machine learning, Python, and Model Evaluation. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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