Asks for

The ad says“Demonstrated experience with MLOps principles, tools, and platforms (e.g., Kubeflow, MLflow, Airflow/Dagster for orchestration).”
The ad says“Experience deploying and maintaining models in a cloud environment (GCP or AWS).”
The ad says“Expertise in SQL and experience working with large-scale data warehouses (Snowflake, BigQuery, or Redshift).”
The ad says“Strong theoretical and practical experience with a wide range of ML models (e.g., classification, clustering, time-series, deep learning).”
The ad says“Strong theoretical and practical experience with a wide range of ML models (e.g., classification, clustering, time-series, deep learning).”
The ad says“Demonstrated experience with MLOps principles, tools, and platforms (e.g., Kubeflow, MLflow, Airflow/Dagster for orchestration).”
The ad says“Strong theoretical and practical experience with a wide range of ML models (e.g., classification, clustering, time-series, deep learning).”
The ad says“Solid understanding of probability, statistics, and experimental design.”
The ad says“Experience deploying and maintaining models in a cloud environment (GCP or AWS).”
The ad says“Demonstrated experience with MLOps principles, tools, and platforms (e.g., Kubeflow, MLflow, Airflow/Dagster for orchestration).”
The ad says“Strong theoretical and practical experience with a wide range of ML models (e.g., classification, clustering, time-series, deep learning).”
The ad says“Demonstrated experience with MLOps principles, tools, and platforms (e.g., Kubeflow, MLflow, Airflow/Dagster for orchestration).”
The ad says“Demonstrated experience with MLOps principles, tools, and platforms (e.g., Kubeflow, MLflow, Airflow/Dagster for orchestration).”
The ad says“Proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).”
The ad says“Proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).”
The ad says“Solid understanding of probability, statistics, and experimental design.”
The ad says“Proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).”
The ad says“Proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).”
The ad says“Expertise in SQL and experience working with large-scale data warehouses (Snowflake, BigQuery, or Redshift).”
The ad says“Proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).”
The ad says“Expertise in SQL and experience working with large-scale data warehouses (Snowflake, BigQuery, or Redshift).”
The ad says“Expertise in SQL and experience working with large-scale data warehouses (Snowflake, BigQuery, or Redshift).”
The ad says“Solid understanding of probability, statistics, and experimental design.”
The ad says“Proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).”
The ad says“Strong theoretical and practical experience with a wide range of ML models (e.g., classification, clustering, time-series, deep learning).”

Company Overview

TENEX is an AI-native, automation-first, built-for-scale Managed Detection and Response (MDR) provider. We are a force multiplier for defenders, helping organizations enhance their cybersecurity posture through advanced threat detection, rapid response, and continuous protection. Our team is composed of industry experts with deep experience in cybersecurity, automation and AI-driven solutions. Backed by leading investors, we are rapidly growing and seeking top talent to join our mission of revolutionizing the AI-Native MDR landscape.
We’re a fast growing startup backed by industry experts and top tier investors led by Crosspoint Capital Partners and also backed by Shield Capital, DTCP (formerly Deutsche Telekom Capital Partners), Deepwork Capital, and the Florida Opportunity Fund. Seed round led by Andreessen Horowitz (a16z). As an early employee, you’ll play a meaningful role in defining and building our culture. Get in on the ground floor. We’re a small but well-funded team that just raised a substantial round – joining now comes with limited risk and unlimited upside.

We’re a fast-growing startup backed by Andreessen Horowitz. As an early employee, you’ll help shape our culture and have meaningful ownership over high-impact initiatives. This is a unique opportunity to join a small but well-funded team on the ground floor as we build the next-generation cybersecurity platform.

We are expanding our engineering organization and seeking a Senior Data Scientist to design, build, and deploy the machine learning models that power our AI-driven cybersecurity platform. This role is foundational in delivering high-fidelity threat detection, automating response actions, and generating predictive security intelligence across TENEX.

Culture is one of the most important things at TENEX.AI—explore our culture deck at culture.tenex.ai to witness how we embody it, prioritizing the irreplaceable collaboration and community of in-person work.

This is an in-person opportunity based in our San Jose, CA office where you will be expected to work out of 4 days a week.

Role Overview

As a Senior Data Scientist, you will be responsible for the end-to-end lifecycle of machine learning models: from ideation and research to production deployment and monitoring. You will leverage large volumes of cybersecurity and operational data to create models that enhance our Managed Detection and Response (MDR) capabilities, including anomaly detection, threat scoring, and automated alert triage.

You’ll work closely with Security Operations, Product, and Data Engineering teams to translate complex security challenges into data science problems, ensuring our AI/ML solutions are effective, scalable, and directly contribute to our clients' security outcomes. This role combines deep analytical rigor with practical engineering to deliver mission-critical AI for cybersecurity.

Job Responsibilities:

AI/ML Model Development

Design, develop, train, and deploy high-performance machine learning models for critical security tasks such as threat detection, anomaly scoring, and behavioral analytics.
Conduct feature engineering and selection on vast, high-velocity streams of security data (logs, network telemetry, endpoint data).
Own the model lifecycle, including versioning, rigorous testing, and continuous improvement through MLOps best practices.

Research & Innovation

Stay up-to-date on state-of-the-art research in data science, deep learning, and security-specific AI to drive platform innovation.
Explore novel statistical methods and machine learning techniques to tackle emerging and sophisticated cyber threats.
Design and implement A/B testing and evaluation frameworks to measure the impact and performance of deployed models.

Data & Feature Engineering

Partner with Data Engineers to define, preprocess, and structure large, complex datasets for model training and inference.
Implement and manage data pipelines specifically for feature extraction and model serving.
Leverage vector databases and RAG (Retrieval-Augmented Generation) pipelines for enhanced security context and large language model applications.

Cross-Functional Collaboration

Work closely with Security Operations to understand real-world threat landscapes and ensure model outputs are actionable and integrated into workflows.
Collaborate with Product Managers to define AI features and translate model performance into measurable business value.
Present complex analytical findings and model behavior clearly to technical and non-technical audiences.

Required Skills & Qualifications:

5+ years of professional experience in Data Science, Machine Learning Engineering, or a related quantitative field.
Strong theoretical and practical experience with a wide range of ML models (e.g., classification, clustering, time-series, deep learning).
Proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).
Expertise in SQL and experience working with large-scale data warehouses (Snowflake, BigQuery, or Redshift).
Demonstrated experience with MLOps principles, tools, and platforms (e.g., Kubeflow, MLflow, Airflow/Dagster for orchestration).
Solid understanding of probability, statistics, and experimental design.
Experience deploying and maintaining models in a cloud environment (GCP or AWS).
Excellent communication skills with the ability to drive projects autonomously and translate business needs into technical requirements.

Desired:

Prior experience applying data science/ML in the cybersecurity or security analytics domain, particularly in MDR or MSSP environments.
Experience with real-time / streaming data systems (Kafka, Pub/Sub, Kinesis) for low-latency threat detection.
Familiarity with the use of Vector Databases and RAG architectures.
Experience with modern analytics engineering frameworks.
Experience working in an early-stage startup environment.

Education & Certifications

Master’s or Ph.D. in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field (or equivalent experience).
Relevant certifications in Data Science or Cloud ML Platforms are a plus.

Why Join Us?

Opportunity to work with cutting-edge AI-driven cybersecurity technologies and Google SecOps solutions.
Collaborate with a talented and innovative team focused on continuously improving security operations.
Competitive salary and benefits package.
A culture of growth and development, with opportunities to expand your knowledge in AI, cybersecurity, and emerging technologies.

Asks for

Values