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The Antitrust Division of the U.S. Department of Justice (DOJ) is responsible for enforcing federal antitrust laws and promoting fair competition in the marketplace.. Apply natural language processing (NLP), computer vision, or other domain-specific algorithms as required by the research.. Utilize cloud platforms and big data technologies (e.g., AWS, Azure, Hadoop, Spark) for efficient data processing and model deployment.. Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI) to present insights effectively.. Strong understanding of big data technologies (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud).
The Antitrust Division of the U.S. Department of Justice (DOJ) is responsible for enforcing federal antitrust laws and promoting fair competition in the marketplace.. Apply natural language processing (NLP), computer vision, or other domain-specific algorithms as required by the research.. Utilize cloud platforms and big data technologies (e.g., AWS, Azure, Hadoop, Spark) for efficient data processing and model deployment.. Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI) to present insights effectively.. Strong understanding of big data technologies (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud).
We are using our big data coupled with machine learning and AI to help highlight the path forward.. The Data Scientist has applied expertise in Python, distributed databases, advanced AI/ML modeling, statistical analysis, and data visualization at scale.. Employ predictive modeling, natural language processing and categorization techniques to increase and optimize customer experience and revenue generation.. Professional experience with python, including python data libraries (numpy, pandas, matplotlib, scikit-learn) with proven ability to employ python for data manipulation, large-scale analytics, and ensemble-based ML development.. Extensive knowledge, application, and experience in creating and implementing recommendation systems, machine learning, NLP, statistics, and deep learning.
The role will apply statistical programming, modeling, visualization techniques, data mining, and forecasting skills to analyze challenging public sector problems.. Examples will include techniques such as machine learning (ML) supervised and unsupervised learning, regression, neural networks and deep learning, natural language processing, etc.. High degree of experience utilizing SAS, R, or Python to support NLP use cases such as Document Summarization, Named Entity Recognition, Sentiment Analysis, and/or Topic Modeling. At least four years of experience developing scalable, production-ready NLP solutions using sci-kit learn, Keras, TensorFlow, PyTorch, Spark NLP.. Experience leveraging transformer architecture to develop NLP models
Citian is a fast growing, venture backed SaaS technology company based in Washington, DC. Our software solutions revolutionize how our transportation systems roads, rail, transit, bicycle, pedestrian operate.. Citian is seeking to hire a Transportation Data Scientist who will play a crucial role in developing and implementing state-of-the-art machine learning models for use cases in traffic safety, mobility, and city planning.. The ideal candidate will exhibit excellent knowledge of the data science and data engineering disciplines with transportation industry experience, including working with large datasets in a Python/SQL environment.. Strong knowledge of Python, SQL, Python data science libraries (Pandas, Numpy, Scikit-learn, etc.). Opportunity to gain valuable experience and make a significant impact in a fast growing, venture-backed tech startup
We are looking for an experienced Machine Learning Engineer to join our team on a temporary, 12 month project.. As a Machine Learning Engineer, you will develop and deploy machine learning models for anomaly detection, processing both structured data and unstructured marketing content to identify suspicious behavior patterns and anomalous activities.. Develop NLP pipelines for analyzing ad content and detecting deceptive marketing claims.. Expert proficiency in Python and ML frameworks (scikit-learn, TensorFlow, PyTorch).. Experience with using anomaly detection techniques for fraud detection.
Machine Learning Engineer (Contract-to-Hire)Remote (U.S. – NYC Tri-state preferable, Eastern Time Hours Preferred)An innovative software product organization is seeking a Machine Learning Engineer to support the design and deployment of models that power advertising personalization and smarter user segmentation.. Leverage tools such as Google Ad Manager and other ad tech platforms to define, manage, and evaluate user cohorts.. What We’re Looking For:3–5+ years of hands-on experience in machine learning, with a focus on advertising, martech, or audience targeting.. Familiarity with ad tech platforms such as Google Ad Manager (GAM), DV360, or similar tools.. Degree in Computer Science, Statistics, Data Science, or a related field—or equivalent professional experience.
Join to apply for the Machine Learning Engineer (TS/SCI) role at Maxar Technologies. Maxar is seeking a talented and driven Machine Learning Engineer to join our innovative team.. Solid understanding of machine learning concepts, algorithms, and libraries (e.g., scikit-learn, TensorFlow, PyTorch).. Experience with natural language processing (NLP), computer vision, or other specialized areas of machine learning.. Sign in to set job alerts for Machine Learning Engineer roles.
This individual will partner closely with senior leadership, product, operations, growth, data engineering and data governance teams to operationalize insights, optimize business performance, and expand CAQH’s capabilities in artificial intelligence (AI), machine learning (ML), and decision science.. Work closely with Data Engineering, Architecture, and Governance teams to ensure data science solutions are interoperable, secure, and aligned with CAQH’s enterprise data ecosystem.. Proficiency in Python, R, SQL, Spark, and data science frameworks (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).. Familiarity with data governance and compliance frameworks (e.g., HIPAA, HITRUST, GDPR).. Bachelor’s degree in computer science, data science, statistics, applied mathematics, or a related field required.
Design, implement, and optimized parallel data processing tools to obtain high quality training and evaluation datasets of various machine learning models.. Utilizing Pytorch, Tensorflow, Spark, and MLlib for machine learning modelling and tool development. Utilizing Spark, Hadoop, Apache Iceberg for massive data processing. Experience with AWS S3, EC2, and GCloud GCP cloud platform. Utilizing Docker, Kubernetes, and Airflow to develop data process pipelines
As an experienced machine learning engineer, you understand good software is more than just a good user experience.. 5+ years of experience as a machine learning engineer building production-grade machine learning solutions with a programming language, such as Python, Rust, Go, Scala, or Java.. Experience with project work in deep learning, computer vision, NLP, or signal processing.. Knowledge of Databricks, PyTorch, Tensorflow, Langchain, or Kubernetes.. As part of the application process, you are expected to be on camera during interviews and assessments.
We are looking for a machine learning engineer to help develop applied ML models and own end-to-end pipelines that will enable us to deliver real-time insights and guidance to key clients this election cycle.. Our pipeline involves using deep learning to fine-tune large, powerful transformer models, and we are excited to be applying the latest advances in AI tools to our unique, private dataset.. Is a strong Python programmer, and is familiar with standard software development tools and best practices.. Has experience with NLP and training transformer ML models using PyTorch, or similar tools.. Is familiar with cloud services, distributed systems, and other DevOps tools (Docker, Kubernetes, Terraform, etc
DMG is a Certified Great Place to Work with a strong, inclusive culture and top-notch benefits.. Apply a variety of advanced analytical techniques including predictive modeling, machine learning, time series analysis, simulation, and optimization.. Masters degree in an analytical field such as Data Science, Computer Science, Applied Mathematics, Operations Research or Economics.. Experience with code version control platforms like GitHub, GitLab, or Azure DevOps. At DMG, youll be part of an amazing team that encourages learning, growth, and advancement.
Leidos is looking for an Enterprise Architect to support a large U.S. Department of Justice (DOJ) program.. The Antitrust Division of the U.S. Department of Justice (DOJ) is responsible for enforcing federal antitrust laws and promoting fair competition in the marketplace.. Expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, Scikit-learn, Keras) and understanding of deep learning and natural language processing (NLP).. Strong understanding of big data platforms such as Hadoop, Apache Spark, and data lakes.. Experience with data visualization tools like Power BI, Tableau, or Looker.
The Senior Data Scientist will lead the development of machine learning and deep learning solutions that power intelligent decision-making and innovative products.. Design, build, and evaluate machine learning and deep learning models for classification, regression, recommendation, NLP, computer vision, and time-series forecasting.. 5+ years of industry experience in applied machine learning, with 2+ years focused on deep learning and neural network applications.. Proficiency in Python and ML libraries such as scikit-learn, XGBoost, TensorFlow, Keras, or PyTorch.. Experience with cloud platforms (AWS/GCP/Azure), containerization, and model serving technologies.
The role will apply statistical programming, modeling, visualization techniques, data mining, and forecasting skills to analyze challenging public sector problems.. Modeling - Select and apply modeling techniques like supervised and unsupervised machine learning, regression, neural networks, and deep learning.. At least four years developing scalable NLP solutions using tools like scikit-learn, Keras, TensorFlow, PyTorch, Spark NLP.. Familiarity with open-source NLP packages such as Gensim, SpaCy, NLTK.. Experience with Databricks and cloud environments, specifically AWS.
Sr. Machine Learning Engineer/Date Scientist (6-Month Project). Join to apply for the Sr. Machine Learning Engineer/Date Scientist (6-Month Project) role at Element. We are looking for an experienced Machine Learning Engineer to join our team on a temporary, 6-month project.. As a Machine Learning Engineer, you will develop and deploy machine learning models for anomaly detection, processing both structured data and unstructured marketing content to identify suspicious behavior patterns and anomalous activities.. Sr Machine Learning Engineer - Fintech Foundation (100% Remote - USA)
Leverage Azure DevOps for continuous integration and continuous deployment (CI/CD) of ML models. Bachelors Degreein Computer Science, Machine Learning, Data Science, or a related field requires; Masters Degree or Ph. D. in Computer Science, Machine Learning, Data Science, or a related field is preferred. Proficiency in Python for constructing data pipelines, and using ML frameworks and libraries such as Keras, PyTorch, Scikit-Learn, TensorFlow, and XGBoost. 2+ years of experience developing in a cloud environment (AWS, GCS, Azure)2+ years of experience with Github, Github Actions, CI/CD, and source control. Experience with deep learning, reinforcement learning, NLP, and LLMs is preferred
Investigate data visualization and summarization techniques for conveying key findings. Hands-on experience in data cleaning and data quality assessment, hypotheses testing, data exploration and mining, visualization techniques, algorithm selection and building predictive models using traditional machine learning algorithms. Some experience with AWS Sagemaker, Google TensorFlow or MS Azure ML. Solid understanding of Tableau or other BI visualization tools, Google Analytics, Adobe Analytics, SAS. Awareness of Narrative Science/ YSEOP/Automated Insights and concepts of NLG/NLP
The ideal candidate will have industry experience working on classification and optimization problems such as payment fraud, click-through rate prediction, click-fraud detection, search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection.. Adapt machine learning methods for modern parallel environments (distributed clusters, multicore SMP, GPU). 8+ years in machine learning, recommendation systems, pattern recognition, NLP, data mining, or AI. Experience with Hadoop, Pig, MapReduce, Sawzall, Bigtable, Hive, Spark. Expertise in machine learning, recommendation systems, pattern recognition, NLP, data mining, or AI