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As a dedicated Director, Data Scientist, you will lead a team of Data Scientists responsible for identifying, scoping, and translating business problems into applied statistical, machine learning, simulation, and optimization solutions to inform actionable business insights and drive business value through automation, revenue generation, and expense and risk reduction. Responsible for ensuring all modeling and machine learning solutions adhere to industry standards, model risk policy, and regulatory expectations. 8 years in predictive modeling, model governance, machine learning and large data analysis., OR Advanced Degree (e.g., Master’s, PhD) in Mathematics, Statistics, Data Science, Computer Science, or related quantitative STEM field (Science, Technology, Engineering and Math) field and 6 years in predictive modeling, model governance, machine learning and large data analysis. Experience with various languages, applications, and technologies (such as SQL, Python, R, Spark, Hadoop etc. Experience in developing and reviewing modeling solutions based on broad range of techniques – e.g., linear and logistic regressions, time series methods, survival analysis, support vector machines, neural networks, decision trees, random forests, gradient-boosting methods, deep learning, k-means and other clustering methods, simulation methods, or other advanced techniques.
Deep understanding of AI/ML concepts with expert knowledge in Large Language Models, Deep Learning, Neural Networks, NLP. MSc or PhD in a quantitative field, e.g., Computer Science, NLP, AI, Machine Learning.. 5+ years of experience in Deep Learning, Neural Networks, Generative AI, Knowledge graph, NLP and relevant frameworks.. Experience with common analytics and data science frameworks: PyTorch, NumPy, sci-kit-learn, pandas, Keras, TensorFlow, etc.. Job Segment: Research Engineer, Training, Computer Science, Compliance, Engineering, Research, Education, Technology, Legal
As the Senior Staff Data Scientist, you will be at the forefront of developing and delivering innovative algorithms that generate actionable business insights for key areas within GE HealthCare, including Finance, Commercial, Supply Chain, Quality, Operational Excellence and Lean, and Manufacturing. PhD in Computer Science, Data Science, Engineering or a STEM related field with a focus on neural networks and computer vision. Proficiency in the latest Python, AWS, Azure, and open-source data science tools such as Jupyter, R, SQL, Hadoop, Spark, TensorFlow, Keras, PyTorch, and Scikit-learn. Experience with image processing techniques and libraries like OpenCV. Ability to continuously track, evaluate, adapt the latest advancements in deep learning techniques and AI/ML research to business use cases across GE HealthCare.
Identify and Ideate on Data & Analytics and Artificial Intelligence initiatives to meet Customer needs and /or achieve & exceed Segment goals. Lead impact tracking for Analytics and AI within the Segments and partner with the Enterprise Data Strategy Director. Recognized expert in multiple data science techniques including experimental design and predictive modeling. Demonstrated Expertise with at least one Data Science environment (R/RStudio, Python, SAS) and at least one database architecture (SQL, NoSQL).. Strong experience with machine learning environments (e.g., TensorFlow, scikit-learn, caret)
Analytical needs can include: data aggregation / creation, data cleaning / manipulation, commercial data science (e.g., geospatial, machine learning, predictive modelling, NLP, GenAI etc.. A minimum of 2 years of experience in applied data science with a solid foundation in machine learning, statistical modeling, and analysis is required for a Data Scientist. Strong knowledge, experience, and fluency in a wide variety of tools including Python with data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch), Spark, SQL; familiarity with Alteryx and Tableau preferred. Technical understanding of machine learning algorithms; experience with deriving insights by performing data science techniques including classification models, clustering analysis, time-series modeling, NLP; technical knowledge of optimization is a plus. e.g., Sagemaker, Azure ML, Kubernetes, Airflow)
Equilibrium Energy is revolutionizing the clean energy transition by developing innovative grid-scale energy storage solutions. Formulate and apply novel machine learning solutions to the energy domain : Tackle complex deep learning & machine learning problems by researching published academic literature, surveying industry techniques & intuition, and executing hands-on experimental testing & modeling. Passion for clean energy and fighting climate change.. 4+ years experience in data science, research science, machine learning, or similar role, applying and adapting deep learning, graph neural networks, or reinforcement learning techniques to time series regression & forecasting problems.. 3+ years experience with python and the supporting computational science tool suite (e.g. numpy, scipy, pandas, scikit-learn, tensorflow, etc.)
The goal of a Machine Learning Engineer at Scale is to bring techniques in the fields of computer vision, deep learning and deep reinforcement learning, or natural language processing into a production environment to improve Scale's products and customer experience. Extensive experience using computer vision, deep learning and deep reinforcement Learning, or natural language processing in a production environment.. Strong programing skills in Python or Javascript, experience in Tensorflow or PyTorch.. Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization.. AWS or GCP) and developing machine learning models in a cloud environment
Research, design, and implement state-of-the-art AI methodologies and techniques to enhance predictive modeling and optimization. Strong expertise in machine learning, deep learning, and statistical modeling techniques, with hands-on experience in developing and deploying AI models. Proficiency in programming languages such as Python and R, along with experience in using AI libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Solid understanding of data preprocessing, feature engineering, and data visualization techniques. Experience with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark) for handling large-scale datasets.
Crowdstrike’s Data Science team is expanding – we are looking for a Senior Machine Learning Engineer to join our growing Data Science organization.. Automate and visualize analyses, results and processes in our artificial intelligence and machine learning pipeline. Python (xgboost, pytorch, tensorflow, scikit-learn, pandas, huggingface). Proven track record working with distributed compute systems (e.g. Spark, Ray, etc) running on a cloud provider such as AWS or GCP. The base salary range for this position in the U.S. is $135.000 - $215.000 per year + variable/incentive compensation + equity + benefits.
We are currently looking for a talented and innovative Applied Science leader to bring our Machine Learning and Artificial Intelligence R&D capabilities to the next level.. Technical depth in machine learning systems (e.g. SageMaker, MLFlow), and deep learning frameworks (e.g. TensorFlow, PyTorch, MXNet etc). Strong publication record in top-tier ML and NLP conferences (e.g. ACL, NAACL, EMNLP, NeurIPS, ICML, AAAI, ICLR, SIGIR etc.. Experience with Big Data technologies such as AWS, Hadoop, Spark, Hive, Lucene/SOLR, or Kafka. Wellness Reimbursement for $300 per quarter for wellness activities including gym memberships, spa massages, workout equipment, meditation apps, and much more.
Director, Artificial Intelligence and Machine Learning (AI/ML) Quality Oversight United States - Maryland - Frederick, United States - California - Santa Monica, United States - California - Oceanside Quality Regular.. Ensure the quality and reliability of Natural Language Processing (NLP) solutions developed for tasks including sentiment analysis and automation. Define quality standards and oversee the development and implementation of deep learning models for complex applications such as image and speech recognition and autonomous systems. PhD in degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field with 8+ years of progressive experience in developing and deploying machine learning models and AI solutions, with a strong emphasis on quality assurance and oversight OR. Hands-on experience with NLP, deep learning frameworks (e.g., TensorFlow, PyTorch), and deploying models in production environments, with a quality-centric approach.
Has obtained a Ph. D. degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Reinforcement Learning, Mathematics, or relevant technical field.. Experience with deep learning frameworks such as Pytorch or Tensorflow.. Experience building systems based on machine learning, reinforcement learning and/or deep learning methods.. Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, or similar.. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Apply statistical and machine learning techniques (clustering, regression, NLP, deep learning, etc. Develop and deploy models using Python or R, leveraging tools like scikit-learn, TensorFlow, and PyTorch.. Experience: 1–3 years in a data science role with hands-on experience in analytics and modeling.. Proficiency in Python (pandas, numpy, scikit-learn) and data visualization tools (Power BI, Tableau, etc. Familiarity with cloud platforms (Azure, AWS, or GCP) and SQL-based data environments.
We are an interdisciplinary team with backgrounds in Computer Science, ML/AI, Electrical Engineering, Computer Engineering, Physics, Neuroscience, Economics, and VLSI Design, united by our passion to solve challenging problems. About the Role: We are seeking a highly skilled Applied ML Research Engineer to research, develop, and deploy solutions to accelerate the chip design process and who is passionate about building, delivering, and seeing their work realized in end-to-end products used to design Qualcomm chips. Strong background in machine learning, deep learning, and statistical modeling. Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn). Experience with big data technologies such as Hadoop, Spark, and cloud platforms like AWS, Azure, or Google Cloud.
Our team has a mix of highly proficient people from multiple fields such as Machine Learning, Data Science, Software Engineering, Operations, and Big Data Analytics. Our Senior Researchers tackle such diverse and challenging projects on Image Quality scoring; Automatic Taxonomy Improvement; Entity Resolution of rich, hierarchical Entities; and Conflict Resolution between different representations of the same Entity. Data Strategy: Partner with the operations and data teams to ensure access to high-quality labeled data, and proactively shape data acquisition strategies where needed. Deep understanding of modern ML approaches including classification, regression, NLP, clustering, deep learning, and/or reinforcement learning. Proficiency with big data processing frameworks such as Hadoop, Spark, and SQL.
State of the art prediction models for estimating food preparation times, batching quality as well as time spent by couriers at restaurants picking up items.. You will be in-charge of solving Uber scale problems with the right techniques like reinforcement learning/deep learning/optimization methods.. Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib. Experience with SQL and database systems such as Hive, Kafka, Cassandra, etc. Experience in modern deep learning architectures and probabilistic models.
As a dedicated Director, Data Scientist, you will lead a team of Data Scientists responsible for identifying, scoping, and translating business problems into applied statistical, machine learning, simulation, and optimization solutions to inform actionable business insights and drive business value through automation, revenue generation, and expense and risk reduction.. Responsible for ensuring all modeling and machine learning solutions adhere to industry standards, model risk policy, and regulatory expectations.. 8 years in predictive modeling, model governance, machine learning and large data analysis., OR Advanced Degree (e.g., Master's, PhD) in Mathematics, Statistics, Data Science, Computer Science, or related quantitative STEM field (Science, Technology, Engineering and Math) field and 6 years in predictive modeling, model governance, machine learning and large data analysis.. Experience with various languages, applications, and technologies (such as SQL, Python, R, Spark, Hadoop etc.). Experience in developing and reviewing modeling solutions based on broad range of techniques – e.g., linear and logistic regressions, time series methods, survival analysis, support vector machines, neural networks, decision trees, random forests, gradient-boosting methods, deep learning, k-means and other clustering methods, simulation methods, or other advanced techniques.
They will independently and collaboratively research, design, develop, and implement innovative AI, Machine Learning, deep learning, NLP, Cloud, and Data Science solutions to advance NYSE's analytics across various business lines.. 2+ years applying AI/ML/NLP/deep learning to financial market data. Strong knowledge of machine learning, AI, deep learning, NLP, and unstructured data analytics. Java, R, MATLAB, Scala, with frameworks like TensorFlow, Caffe, Spark, Hadoop. Knowledge of data management, analytics middleware, cloud computing (AWS), data visualization tools, GPU programming, and fog computing is a plus
Design, develop, and deploy ML models and AI solutions across various domains such as NLP, computer vision, recommendation systems, time-series forecasting, etc.. Integrate ML pipelines with production systems using tools like MLflow, Airflow, Docker, or Kubernetes.. 4–8 years of hands-on experience in machine learning, deep learning, or applied AI.. Proficiency in Python and ML libraries/frameworks (e.g., Scikit-learn, TensorFlow, PyTorch, XGBoost).. Familiarity with cloud platforms (AWS, GCP, or Azure) and ML tools (SageMaker, Vertex AI, etc
Designs and implements Machine Learning (ML) and Deep Learning (DL) algorithms across multiple projects.. Programs ML frameworks using Python and R. Develops and models software solutions utilizing Natural Language Processing (NLP), Information Retrieval, Machine Comprehension, Question Answering/Conversational AI, Reinforcement Learning, Knowledge Graphs, Causal Inference, and Design of Experiments.. Conducts exploratory data analysis, unstructured data analysis, predictive analytics, and prescriptive analytics using Big Data, NLP, and chatbot technologies (Elasticsearch and Solr).. Expertise in predictive modeling, training, and evaluating ML algorithms using Python and frameworks like scikit-learn, TensorFlow, PyTorch, or Keras, especially in Conversational AI and Search.. Writing scalable, production-grade Python code, optimizing for performance, and low latency through techniques like quantization and knowledge distillation.