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Causal Ai Algorithm Jobs (NOW HIRING)

... causal AI, operations research, computer science, Mathematics, business analytics, or knowledge ... and algorithmic design experience in Python (3+ years) * Proficient with Amazon AWS Sagemaker ...

Senior AI/Machine Learning Specialist

Maplewood, MN · On-site

$89K - $109K/yr

Strong Python skills and experience translating Python algorithms or prototypes into production software. * Exposure to symbolic AI, neural-symbolic systems, cognitive architectures, causal reasoning ...

Senior AI/Machine Learning Specialist

Maplewood, MN · On-site

$89K - $109K/yr

Strong Python skills and experience translating Python algorithms or prototypes into production software. * Exposure to symbolic AI, neural-symbolic systems, cognitive architectures, causal reasoning ...

... advanced AI, recommendation systems, and adtech. Recognized by Fast Company as #32 on the Top ... Design and analyze experiments (A/B, switchback) to prove causal impact in a marketplace setting.

... advanced AI, recommendation systems, and adtech. Recognized by Fast Company as #32 on the Top ... Design and analyze experiments (A/B, switchback) to prove causal impact in a marketplace setting.

Alembic's science team implements causal mathematics -- the algorithms our whole product rests on ... About Us Alembic is the pioneering Causal AI platform. We help the world's largest enterprises move ...

Our highly skilled team of Applied AI/ML Scientists specialize in developing algorithmic solutions ... Experience with relevant technical methods (causal inference, predictive/LTV modeling ...

Showing results 41-60

Causal Ai Algorithm information

See salary details

$25K

$80.3K

$163.5K

How much do causal ai algorithm jobs pay per year?

As of Sep 9, 2026, the average yearly pay for causal ai algorithm in the United States is $80,287.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,500.00 and $103,000.00 per year, depending on experience, location, and employer.

What is the difference between Causal Ai Algorithm vs Data Scientist?

AspectCausal Ai AlgorithmData Scientist
Required CredentialsKnowledge of causal inference, statistics, machine learningDegree in data science, statistics, computer science
Work EnvironmentResearch-focused, algorithm development, data analysisData analysis, model building, reporting
Industry UsageDeveloping causal models for decision-makingAnalyzing data, creating predictive models

The main difference is that Causal Ai Algorithms focus on identifying cause-effect relationships using specialized techniques, while Data Scientists analyze data to build predictive models and generate insights. Both roles require strong statistical skills, but Causal Ai Algorithms are more specialized in causal inference methods.

What other helpful pages are available for Causal Ai Algorithm?

Other pages related to Causal Ai Algorithm:

Infographic showing various Causal Ai Algorithm job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 76% Full Time, 19% Part Time, and 4% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $80,287 per year, or $38.6 per hour.

Data Scientist

Washington, DC • On-site

JS Consulting
Custom Software Development Services • 1 - 10 employees

Contractor

Re-posted 12 hours ago


Job description

Job Title- Data Scientist

Project Location – Onsite in Washington, District of Columbia

Duration- 6+ months contract

Visa- USC

Must have PHD

 Minimum Qualifications:

  • 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, generative AI, symbolic AI, causal AI, operations research, computer science, Mathematics, business analytics, or knowledge management.
  • Demonstrated experience programming with R/Python, Linux, and Spark in AWS cloud environment, or knowledge and algorithmic design experience in Python (3+ years)
  • Proficient with Amazon AWS Sagemaker, Jupyter Notebook and Python Scikit, Deep Learning, Machine Learning tools such as TensorFlow
  • Experience with image processing models such as Coco, CLIP, ResNet or comparable models
  • Demonstrated experience with machine learning techniques including natural language processing, and Large language Models (GPTv4-o1, o3, OpenAI APIs, Llama, Claude, etc).
  • Experience developing AI agents and development proficiency using agentic programming
  • Proficient in Natural language processing (NLP) and Natural language generation (NLG) including prior projects in any of the following categories: top modeling of text, sentiment analysis of text, part of speech tagging, Name Entity Recognition (NER), Bag of Words, text extraction
  • Experience building and working with any of these components: Vector DB, BERT, RoBERTa (or comparable tools), Spacy, LLM and GenAI tools. Experience with LoRA, LangChain, RAG, LLM Fine Tuning and PEFT, Knowledge Graphs.
  • Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI development architectures with Human-in-the-Loop (HITL
  • Demonstrated experience with SQL and any relational database technologies, such as Oracle, PostgreSQL, MySQL, RDS, Redshift, Hadoop EMR, Hive, etc.
  • Demonstrated experience processing structured and unstructured data sources, data cleansing, data normalization and prep for analysis
  • Demonstrated experience with code repositories and build/deployment pipelines, specifically Jenkins and/or Git/GitHub/GitLab.
  • Demonstrated experience using Tableau, or Kibana, Quicksights or other similar data visualizations tools.
  • Very comfortable working with ambiguity (e.g. imperfect data, loosely defined concepts, ideas, or goals)

 Qualifications & Requirements

  • Education: MS in Computer Science, Statistics, Math, Engineering, or related field, PhD required.
  • 3+ years of relevant experience in building large scale machine learning or deep learning models and/or systems
  • 1+ year of experience specifically with deep learning (e.g., CNN, RNN, LSTM)
  • 1+ year of experience building NLP and NLG tools.
  • Experience with wide range of LLMs (Llama, Claude, OpenAI, Cohere, etc.), LoRA, LangChain, RAG, LLM Fine Tuning and PEFT are preferred.
  • Demonstrated skills with Jupyter Notebook, AWS Sagemaker, or Domino Datalab or comparable environments
  • Passion for solving complex data problems and generating cross-functional solutions in a fast-paced environment
  • Knowledge in Python and SQL, object oriented programming, service oriented architectures
  • Strong scripting skills with Shell script and SQL
  • Strong coding skills and experience with Python (including SciPy, NumPy, and/or PySpark) and/or Scala.
  • Knowledge and implementation experience with NLP techniques (topic modeling, bag of words, text classification, TF/IDF, Sentiment analysis) and NLP technologies such as Python NLTK, or Spacy or comparable technologies
  • Knowledge and implementation experience with statistical and machine learning models (regression, classification, clustering, graph models, etc.)

 Preferred Qualifications

  • Hands on experience building models with deep learning frameworks like Tensorflow, Keras, Caffe, PyTorch, Theano, H2O, or similar
  • Experience with LLM Agents, Agentic programming
  • Experience with search architecture (for instance: Solr, ElasticSearch, AWS OpenSearch)
  • Experience with building querying ontologies such as Zeno, OWL, RDF, SparQL or comparable are preferred
  • Knowledge & experience with microservices, service mesh, API development and test automation are preferred
  • Demonstrated experience using Docker, Kubernetes, and/or other similar container frameworks are preferred

 Additional Job Qualifications:

  • Ability to translate business ideas into analytics models that have major business impact.
  • Demonstrated experience working with multiple stakeholders.
  • Demonstrated communication skills, e.g. explaining complex technical issues to more junior data scientists, in graphical, verbal, or written formats.
  • Demonstrated experience developing tested, reusable and reproducible work.