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Data Scientist Deep Learning Jobs (NOW HIRING)

Conversica, Inc

Data Scientist

Seattle, WA · On-site

Conversica is seeking talented and passionate data scientists to help us evolve our artificial ... Training in deep learning approaches and natural language processing * Advanced proficiency with ...

Stellar IT Solutions

Data Scientist

Saint Louis, MO · Remote

Data Scientist Remote 12 Month Contract to hire Data Scientist - Profile Overview This is a data ... Less focus on deep learning, GenAI, etc. * More focus on data foundations + analytics reliability

Stellar IT Solutions LLC

Data Scientist

Saint Louis, MO · Remote

$55 - $60/hr

Data Scientist Remote 12 Month Contract to hire Data Scientist - Profile Overview This is a data ... Less focus on deep learning, GenAI, etc. * More focus on data foundations + analytics reliability

Vytwo

Data Scientist - INDIA

Prosper, TX · On-site +1

Data Science Structured Data / Text Data (NLP & GenAI) About the Role We are seeking a highly ... Text / Unstructured Data (NLP & GenAI) Building lowlatency realtime systems using deep learning ...

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All JobsData Scientist Deep Learning Jobs

Data Scientist Deep Learning information

See salary details

$37.5K

$122.7K

$196.5K

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

As of Jun 18, 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 are the key skills and qualifications needed to thrive as a Data Scientist Deep Learning, and why are they important?

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.

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.

What are Data Scientist Deep Learning roles?

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.

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.
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 June 2026, with employment types broken down into 33% As Needed, and 67% Full Time. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

ML Research Scientist -Deep Learning & Transformer Architectures

Millennium Management LLC

New York, NY • On-site

$150K - $200K/yr

Full-time

Posted 11 days ago

Millennium Management rating

7.7

Company rating: 7.7 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

Job description

ML Research Scientist -Deep Learning & Transformer Architectures
Please direct all resume submissions to QuantTalentUS@mlp.com and reference REQ-29605 in the subject.
Overview
As part of a long-term research agenda within a newly formed systematic equities pod, we are building a proprietary Transformer-based model trained on tokenized intraday market data for next-token prediction of price movements.
We are seeking an exceptional ML research scientist with deep expertise in Transformer architectures and large-scale model training. You will design, implement, and train a custom decoder-only Transformer from scratch -not fine-tune an existing LLM, but build a purpose• built architecture for financial time-series.
This is a long-term research project with significant computational resources. The successful candidate will have a PhD in machine learning or a related field and demonstrated ability to implement Transformer architectures from first principles.
Principal Responsibilities
• Design and implement a custom decoder-only Transformer architecture optimized for tokenized financial time-series data
• Develop a novel tokenization scheme for intraday market data: price movements, volume, order flow, and cross-sectional features
• Implement efficient training pipelines using PyTorch with mixed-precision training, gradient checkpointing, and multi-GPU parallelism
• Design attention mechanisms adapted to financial data: temporal attention patterns, cross-asset attention, and multi-scale representations
• Build evaluation frameworks for next-token prediction accuracy, signal quality, and trading performance
• Implement inference optimization for low-latency production deployment: model quantization, KV-cache, speculative decoding
• Conduct rigorous ablation studies to validate architecture choices and training methodology
• Collaborate with the team to integrate model predictions into the live trading pipeline
• Document research methodology, experimental results, and architectural decisions
Required Skills / Qualifications
• PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field with a focus on deep learning
• Demonstrated ability to implement Transformer architectures from scratch (not just finetuning pre-trained models)
• Deep understanding of attention mechanisms, positional encodings, tokenization strategies, and training dynamics
• Expert-level PyTorch skills including custom modules, training loops, mixed-precision, and multi-GPU training
• Strong mathematical foundations: linear algebra, probability theory, optimization, information theory
• Experience training models at scale (100M+ parameters)
• Strong programming skills in Python and C++ for performance-critical components
• Self-directed researcher capable of defining and executing a multi-month research agenda
• Familiarity with Al-assisted development tools (Cursor, Claude Code)
Preferred Skills / Experience
• Experience applying deep learning to financial data or time-series forecasting
• Familiarity with tokenizatlon approaches for continuous or non-text data
• Published research in top ML venues (NeurlPS, ICML, ICLR) or equivalent industry experience
• Knowledge of market microstructure and intraday trading dynamics
• Experience with model compression, quantization, and inference optimization
Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future. When finalizing an offer, we take into consideration an individual's experience level and the qualifications they bring to the role to formulate a competitive total compensation package.

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