Data Scientist, Client Analysis (DS-II)About the role:We are currently looking for a Data Scientist (DS-II) to join our Data Science, Client Analysis team to be based remotely in the USA.
In this unique role as Data Scientist, you will leverage your experience developing algorithms and analyzing massive data sets to tell compelling data stories while also working in partnership with the go-to-market team to drive revenue generation for Socure by ensuring that potential and existing customers can understand - through deep analysis - the benefit and value of Socure's products throughout the pre and post sales cycle.
This is an excellent opportunity for a Data Scientist who thrives when they're able to communicate a data story to an audience (tech and non-tech), and who is not satisfied only working behind the scenes. Data Scientists on the Client Analysis team are compensated for their part in driving new business with a generous incentive bonus opportunity on top of competitive base salary.
Responsibilities:- Analyze massive customer data sets, come up with actionable insights, and explain how our models perform and how we can help our customers build optimal risk management policies
- Act as a Data Science advocate: deeply understand Socure's solutions (products, models and algorithms) and customers' risk challenges to educate and influence from a scientific perspective
- Create convincing data stories to showcase how we can help our customers eliminate identity fraud, streamline their end-user experience, and support their risk management efforts holistically
- Provide thought leadership and be the customer's voice for building the next generation of Socure's core models and product
- Create tools and automated workflows to improve the speed, accuracy, and reproducibility of our analysis
- Partner closely with GTM teams (Sales, Solution Consultants, Account Managers and Technical Account Managers) to derive creative ways of analysis and identify opportunities for upsell and product optimization
- Build ML models when it is advantageous to explore different approaches over the established models in production
Qualifications:- You have a degree in a quantitative field, or equivalent work experience - advanced degree is preferred.
- You have 2+ years experience in data science, data analysis, or analytics engineering
- You have extensive theoretical and practical understanding of state-of-the-art supervised and unsupervised machine learning methods
- You know how to write clean, performant code in Python, and have 2+ years experience working with massive real-life datasets
- You have experience querying relational databases with SQL
- You have experience working with cloud tools and technologies in AWS, Azure, Databricks, or GCP
- You know how to tell compelling stories with data (dashboarding, building interactive data stories and actionable presentations)
- You are able to explain very complex algorithms such as ML/AI models, and analyses to non-technical audiences
- You are an excellent verbal and written communicator and are comfortable delivering presentations with clear, complete messages
- You thrive in a remote startup work environment and are excellent at setting clear goals and holding yourself accountable
- You have experience in identity verification and fraud prevention (strongly preferred)
Technologies we useThe Data Science Client analysis team works across customer data, fraud and identity analytics, model evaluation optimizing workflows, monitoring/alerts, reporting and more. An ideal candidate will have experience with several of the technologies below and be excited to learn the rest.
- Python ecosystem: PySpark, Pandas, NumPy, H2O, SHAP, Seaborn, Jupyter, and related data-science libraries
- SQL and data warehouses: SQL, Redshift, Snowflake, and S3
- Data processing: Databricks, Apache Spark, Apache Arrow, and Amazon EMR
- Data pipelines and automation: Airflow, batch processing, SFTP/PGP, webhook data, and automated daily/weekly reporting
- Model evaluation and risk analytics: backtesting, train/holdout/OOT analysis, performance monitoring, threshold tuning, and model/rule QA
- Visualization and storytelling: Tableau, Looker, dashboards, and interactive presentations
- ML/AI workflows: supervised and unsupervised learning, transformers, clustering, graph analytics, explainability, and LLM/agentic workflows
- Engineering and analytical practices: Git-based version control, production-quality and reproducible analysis, automated data-quality and validation testing, API integrations, and working with structured data formats such as JSON.
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