# Senior Data Scientist - Digital Intelligence, Device Signals

**Company:** [Socure](https://hotfix.jobs/companies/socure)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $150k – $185k/yr
**Experience:** 6+ years
**Skills:** Python, SQL, Spark, Pyspark, scikit-learn, Xgboost, TensorFlow, Machine Learning, Anomaly Detection, A/B Testing
**Posted:** 2026-04-01

> Senior Data Scientist develops ML models and features using device, network, and behavioral data for fraud prevention and identity verification. Requires 6+ years experience, Master's in quantitative field, Python/SQL proficiency, and production ML deployment expertise.

## Job Description

## What You'll Do
- Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention—balancing precision, recall, and real-world adversarial dynamics.
- Contribute to the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data).
- Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse.
- Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques.
- Partner with engineering, product, and risk teams to contribute to data architecture decisions, signal collection, and planning.
- Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust.
- Contribute to team standards for ML explainability, risk evaluation, and feature logging.
- Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences.
- Mentor junior data scientists and participate in cross-functional working groups.

## What You Bring
- Master’s degree (or equivalent practical experience) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- 6+ years of experience in data science or applied machine learning, including experience working in production environments.
- Excellent **SQL** skills and extensive experience with large-scale databases and data modeling.
- Proven track record of deploying and maintaining ML models in live systems, ideally involving streaming or near-real-time data.
- Proficiency in **Python** and distributed computing tools (e.g., **Spark**, **PySpark**).
- Hands-on experience with ML frameworks such as **scikit-learn**, **XGBoost**, **TensorFlow**, or similar.
- Excellent communication skills—able to explain complex technical results to non-technical stakeholders and senior leadership.
- Experience designing and interpreting experiments, working with real-world noisy datasets, and applying sound validation techniques to assess model robustness.
- Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights that influence product or risk strategies.
- Strong judgment across data quality, model selection, and business impact tradeoffs.
- Collaborative mindset and experience working cross-functionally with product, engineering, and analytics teams.

## Preferred Qualifications
- Background in fraud detection, behavioral biometrics, anomaly detection, or adversarial modeling.
- Experience with high-cardinality feature engineering techniques (e.g., frequency/target encoding, embeddings).
- Familiarity with privacy-preserving or robust ML techniques.
- Knowledge of browser/mobile fingerprinting, VPN/proxy detection, or telemetry signal processing.

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