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Data Scientist Deep Learning Jobs (NOW HIRING)

Tenex.AI Inc

Senior Data Scientist

San Jose, CA · On-site

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 ...

Doist

Image Data Scientist

South San Francisco, CA · On-site

$120 - $180/hr

  • Medical

  • Dental

  • Vision

  • Retirement

Our client, a world leader in life sciences and biotechnology, is looking for a "Image Data ... Expertise with deep learning frameworks such as PyTorch, TensorFlow, or Keras. * Experience working ...

New

Apex Informatics

AI Data Scientist

... on deep learning and LLM approaches • Stay abreast of leading-edge technologies in machine ... or data science: data analysis, algorithm design, model architecture specification, machine ...

AIToolboard

Data Scientist Level 3

Suitland, MD · On-site

$94.40 - $198.20/hr

  • Medical

  • Retirement

  • PTO

Apply deep learning and natural language processing (NLP) techniques to develop sophisticated data models and algorithms.\* Leadership and Mentorship: Lead and mentor junior data scientists ...

Augment

Principal Scientist, Data

Houston, TX

$80 - $83/hr

Data Scientist - AI/ML Focus Worksite: Onsite Monday-Thursday (Mandatory) - Houston, TX Must-Have ... Design, train, fine-tune, and evaluate machine learning and deep learning models--including LLMs ...

JS Consulting

Data Scientist

Washington, DC · On-site

Work or educational background in one or more of the following areas: machine learning, computational linguistics, deep learning, ratification intelligence, data science and/or data analytic ...

Accroid Inc

Data Scientist

Springfield, VA · On-site

Data Scientist (Active TS/SCI Clearance) Top Mandatory Skills * 8+ years of experience in Data ... Experience developing and deploying Machine Learning (ML), Deep Learning (DL), Natural Language ...

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Data Scientist Deep Learning information

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$122.7K

$196.5K

How much do data scientist deep learning jobs pay per year?

As of Aug 19, 2026, the average yearly pay for data scientist deep learning in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a data scientist deep learning?

Data Scientist Deep Learning roles focus on designing, building, and implementing deep learning models to solve complex problems using large datasets. These professionals apply neural networks and advanced machine learning techniques to tasks such as image recognition, natural language processing, and predictive analytics. They work with programming languages like Python, use frameworks such as TensorFlow or PyTorch, and often collaborate with cross-functional teams to turn data insights into actionable solutions. Strong mathematical, statistical, and programming skills are essential for success in this role.

What are the key skills and qualifications needed to thrive as a data scientist deep learning?

To excel as a Data Scientist specializing in Deep Learning, you need a strong background in mathematics, statistics, and programming (often Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, as well as experience in handling large datasets and cloud platforms, is essential, and certifications in machine learning can be advantageous. Analytical thinking, problem-solving, and effective communication are crucial soft skills for interpreting data results and collaborating with cross-functional teams. These skills and qualities are vital for building advanced AI models, deriving actionable insights, and driving innovation in data-driven organizations.

How do data scientist deep learning professionals typically collaborate with other teams in a tech organization?

Data Scientist Deep Learning professionals frequently work cross-functionally, partnering with data engineers to prepare and optimize data pipelines, collaborating with machine learning engineers to deploy and scale models, and communicating findings to product managers and stakeholders in accessible terms. This collaborative environment ensures that deep learning solutions are both technically robust and aligned with business goals. Regular meetings and agile workflows help facilitate smooth communication and integration of deep learning models into production systems.

What is the difference between Data Scientist Deep Learning vs Data Scientist Machine Learning?

AspectData Scientist Deep LearningData Scientist Machine Learning
Required CredentialsBachelor's/Master's in CS, Data Science, or related; experience with neural networksBachelor's/Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch, AI-focused projects, neural network developmentData analysis, predictive modeling, algorithm development
Industry UsageAI, computer vision, NLP, speech recognitionFinance, marketing, healthcare, general analytics
Common Search/ComparisonYesYes

Data Scientist Deep Learning specializes in neural networks and AI-driven models, often working on complex tasks like image recognition and NLP. Data Scientist Machine Learning covers a broader range of algorithms and applications, including predictive analytics and traditional machine learning models. Both roles require strong programming skills and statistical knowledge, but Deep Learning roles focus more on neural network frameworks and AI-specific tools.

More about Data Scientist Deep Learning jobs

What are the most commonly searched types of Data Scientist Deep Learning jobs?

The most popular types of Data Scientist Deep Learning jobs are:

What job categories do people searching Data Scientist Deep Learning jobs look for?

The top searched job categories for Data Scientist Deep Learning jobs are:

Data Scientist Deep Learning jobs near you
Infographic showing various Data Scientist Deep Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Senior Data Scientist

Tenex.AI Inc

San Jose, CA • On-site

Full-time

Re-posted 26 days ago

Job description

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.

Apply