Data Scientist

About Us
PathID is an early-stage, stealth-mode cybersecurity startup leveraging generative AI, advanced data analytics, and cutting-edge solutions to transform how organizations gain actionable insights from their data. We’ve raised $17 M in seed funding from premier investors—Cyberstarts (backers of Wiz, Noname Security, Transmit Security, Cyera) and Boldstart (investors in Snyk, BigID). Our founders are serial entrepreneurs with multiple exits, and we’re based in vibrant Tel Aviv near the HaShalom train station.

Position Overview

We are looking for a Data Scientist to join our Research Team. In this role, you will research and develop production-grade models and data pipelines that transform raw enterprise signals into high-fidelity, actionable insights. You will own the full lifecycle of model development—from experimentation to deployment and monitoring—in service of our AI-powered platforms that drive mission-critical decisions.

Key Responsibilities

• Production ML Modeling – Design, build, and iterate on scalable statistical, heuristic and machine learning models using supervised and unsupervised techniques to uncover patterns, anomalies, and predictive signals across enterprise-scale datasets.
• Feature Engineering & Enrichment – Design meaningful features by enriching raw data with organizational, temporal, and environmental context to improve model performance.
• Model Deployment & Monitoring – Deploy models to production using modern MLOps practices; monitor performance, detect drift, and retrain as needed to maintain reliability and accuracy.
• Cross-Functional Collaboration – Work closely with Software Engineers, Product Managers, and domain experts to ensure models are integrated into applications and workflows with measurable impact.
• Research & Innovation – Explore emerging techniques in ML, data representation, and weak supervision to improve model generalization, interpretability, and signal fidelity.
• Cloud & API Integration – Leverage cloud-native tools (AWS, Azure, GCP) and APIs to scale model inference, automate retraining, and integrate predictions into end-user applications.
• Data Pipeline Engineering – Build robust, automated data pipelines (ETL/ELT) that prepare and structure data for modeling and serve features into production systems.

Qualifications & Skills

• 3+ years of experience building and deploying machine learning models in production environments.
• Expert in Python and ML libraries such as Scikit-learn, XGBoost, LightGBM, or TensorFlow/PyTorch.
• Strong understanding of data structures, algorithms, and software engineering fundamentals.
• Proficient in SQL and experience with distributed computing frameworks (e.g., Spark, Dask).
• Experience with feature stores, ML pipelines, and workflow orchestration tools (e.g., Airflow, Prefect).
• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related technical field.
• Ability to break down complex problems and communicate technical solutions clearly to diverse stakeholders.

Bonus Experience

• Experience integrating models into backend systems or real-time applications.
• Familiarity with monitoring and observability platforms (e.g., Prometheus, Grafana, Splunk).
• Exposure to graph-based machine learning, time-series forecasting, or semi-supervised learning.
• Understanding of IAM, compliance frameworks, or secure machine learning practices.
• Experience incorporating model outputs into GenAI or LLM-enabled workflows.

Why Join Us?

• High-Impact Role – Develop core intelligence powering customer-facing applications and decision automation.
• Cutting-Edge Stack – Work at the intersection of ML, data engineering, and cloud automation.
• Rapid Deployment – Ship models into production quickly and see your work deliver real-world results.
• Career Acceleration – Contribute to a fast-paced, well-funded startup where technical ownership and innovation are celebrated.