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Socure is building identity trust infrastructure for the digital economy, verifying identities in real time and stopping fraud before it starts. This Data Scientist II role joins the Client Analysis team to leverage data science expertise in partnership with go-to-market teams to drive revenue and customer value.
You will analyze large customer datasets to extract actionable insights, explain model performance, and help customers build optimal risk management policies. Key responsibilities include: developing and communicating data stories to both technical and non-technical audiences; acting as a data science advocate to educate customers on Socure's solutions and their risk challenges; creating compelling analyses showing how Socure helps eliminate identity fraud and streamline user experience; providing thought leadership on next-generation model development; building automated tools and workflows for analysis; partnering with Sales, Solution Consultants, Account Managers, and Technical Account Managers to identify upsell and optimization opportunities; and building ML models when advantageous.
The role emphasizes communication and storytelling—you'll explain complex ML/AI models and analyses to non-technical audiences, create dashboards and interactive presentations, and deliver clear presentations. You'll work across customer data, fraud and identity analytics, model evaluation, monitoring, and reporting. The tech stack includes Python (PySpark, Pandas, NumPy, H2O, SHAP, Seaborn, Jupyter), SQL and data warehouses (Redshift, Snowflake, S3), data processing (Databricks, Apache Spark, EMR), data pipelines (Airflow), model evaluation tools, visualization platforms (Tableau, Looker), and ML/AI workflows including supervised/unsupervised learning, transformers, clustering, and LLM/agentic approaches.
This is an excellent fit for data scientists who thrive communicating insights to diverse audiences and want to directly influence revenue and product strategy. Compensation includes a competitive base salary plus a generous incentive bonus tied to business impact.
REQUIREMENTS:
- Degree in a quantitative field or equivalent work experience (advanced degree preferred)
- 2+ years experience in data science, data analysis, or analytics engineering
- Extensive theoretical and practical understanding of state-of-the-art supervised and unsupervised machine learning methods
- Clean, performant code in Python; 2+ years working with massive real-life datasets
- Experience querying relational databases with SQL
- Experience with cloud tools and technologies (AWS, Azure, Databricks, or GCP)
- Ability to tell compelling stories with data (dashboarding, interactive data stories, actionable presentations)
- Ability to explain complex algorithms and analyses to non-technical audiences
- Excellent verbal and written communication; comfortable delivering presentations
- Thrive in remote startup environment; excellent at setting goals and accountability
- Experience in identity verification and fraud prevention (strongly preferred)
