# Data Scientist ll

**Company:** [Socure](https://hotfix.jobs/companies/socure)
**Location:** Carson City, NV, San Francisco, CA, Seattle, WA, New York, NY, Washington, DC
**Role:** Data Science
**Salary:** $140k – $170k/yr
**Experience:** 5+ years
**Skills:** Python, SQL, Machine Learning, feature engineering, Model Evaluation, pandas, NumPy, scikit-learn, xgboost, Spark, pyspark, Fraud Detection, Anomaly Detection
**Posted:** 2026-07-16

> Develop ML features, models, and risk signals from device, network, browser, and behavioral telemetry to improve fraud detection and identity verification. Requires 5+ years in data science/applied ML, strong Python/SQL skills, and experience with noisy telemetry and production ML workflows.

## Job Description

## Job Responsibilities
- Develop machine learning features, models, and analytical methods for device, network, browser, mobile, session, and behavioral intelligence.
- Work on scoped fraud and identity risk problems where data quality, labels, telemetry coverage, and product tradeoffs need careful analysis.
- Build features from large-scale, high-cardinality, sparse, noisy, and platform-dependent telemetry.
- Analyze signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, and device or session fragmentation.
- Design and execute validation analyses, including train/test splits, holdout checks, leakage review, drift assessment, customer impact analysis, and feature stability review.
- Use supervised, unsupervised, statistical, and heuristic approaches to identify durable fraud and identity risk signals.
- Investigate imperfect labels, delayed outcomes, instrumentation gaps, and changing fraud patterns to distinguish useful signal from data artifacts.
- Partner with senior data scientists, engineering, product, risk, and platform teams to clarify requirements, prepare data, implement features, and support production rollout.
- Contribute to model documentation, feature definitions, explainability materials, dashboards, and production-readiness reviews.
- Communicate methods, assumptions, findings, limitations, and recommendations clearly to technical and cross-functional stakeholders.
- Support junior data scientists and analysts through code review, analytical feedback, and sharing effective modeling and validation practices.

## Job Requirements
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field, or equivalent practical experience.
- 5+ years of experience in data science, applied machine learning, statistical modeling, analytics engineering, or a related technical role.
- Experience building, evaluating, and improving machine learning models, features, analytical pipelines, or risk signals.
- Strong SQL skills and experience working with large-scale, complex datasets.
- Strong proficiency in Python and experience with data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.
- Experience with distributed data processing tools such as Spark, PySpark, Databricks, or equivalent frameworks.
- Solid understanding of supervised learning, unsupervised learning, feature engineering, model evaluation, statistical validation, and experiment analysis.
- Ability to work with noisy data, imperfect labels, missing values, instrumentation gaps, and changing data distributions.
- Strong analytical judgment across data quality, feature design, model selection, explainability, and business impact.
- Experience collaborating with engineering, product, analytics, or risk teams to move data science work toward production or operational use.
- Clear communication skills, including the ability to explain technical work, assumptions, tradeoffs, and results to non-specialist stakeholders.
- Ability to operate independently on defined problem areas while seeking guidance appropriately on ambiguous or high-risk decisions.

## Preferred Qualifications
- Background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.
- Experience with device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, VPN/proxy detection, or telemetry signal processing.
- Experience developing features from high-cardinality categorical data using techniques such as aggregation, frequency encoding, target encoding, embeddings, graph features, or representation learning.
- Familiarity with production ML workflows, model monitoring, feature monitoring, or batch and near-real-time decisioning systems.
- Experience with dashboarding, model explainability, feature documentation, or customer-impact analysis.
- Interest in adversarial behavior, fraud patterns, telemetry quality, and applied ML systems that operate in real-world production environments.

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