Senior Data Scientist

About the role

This role leads machine learning for document verification and fraud detection. It analyzes product performance and fraud trends to guide modeling directions. The role partners across teams to build, deploy, and monitor production models and evaluation frameworks.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Partnership with engineering deploys, monitors, and continuously improves production models. Analytics and experimentation frameworks support data-driven decisions across product and data science teams. Analytics infrastructure enables model development and cross-functional collaboration on identity trust and fraud prevention. Technical findings influence product and modeling decisions for identity verification and fraud detection challenges.

Requirements

An MS/PhD (or equivalent experience) in Computer Science, Statistics, Data Science, or a related field is required. At least 5 years of experience in machine learning, data science, fraud analytics, or product analytics is required. Strong experience developing and evaluating production ML models at scale is required. Expert SQL and Python skills for data analysis, modeling, and tooling are required. Experience with Databricks, Spark, AWS Sagemaker, or similar data platforms is required. A strong understanding of experimentation, statistical analysis, and ML evaluation measures model performance.

Nice to have

Preference for experience with computer vision, deep learning, or transformer-based models for document and biometric tasks is given. Designing and analyzing A/B tests, online experiments, and statistical evaluations of product or machine learning performance is preferred. Defining product KPIs and building dashboards to monitor product performance, customer behavior, and operational metrics for identity workflows is preferred.

Practical notes

This role operates within a hybrid schedule based in San Francisco. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Work in this area depends on statistical modeling, experimentation, and data infrastructure to address real-world problems. Professionals commonly use SQL, Python, and cloud platforms for large-scale data and model workflows. Success relies on close collaboration with product, engineering, and operations teams to align models with business goals.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Socure is a digital identity verification company that uses machine learning and AI to provide real-time identity fraud prevention. Founded in 2012, Socure serves over 1,800 customers.