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Our Digital Data Science Department is focused on building predictive solutions to support our mobile app and online banking operations.. Combine knowledge of data science techniques with industry research on solutions in the mobile banking sector, including but not limited to logistic regression, random forest, XGBoost, neural networks, NLP, k-means clustering, ARIMA, and prophet forecasting. Programming abilities in SQL, Python, R, or similar languages in data exploration, data preparation, modeling, prediction, and statistical analysis. Ability to learn and use data and cloud environments such as Azure and Databricks. Strong knowledge of statistics and machine learning techniques and tools including logistic regression, XGBoost, neural networks, NLP, and clustering
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
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
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.
We're looking for top-tier talent and offer the flexibility you need to thrive and deliver lasting impact.. 12+ years of experience in roles involving technology-driven process improvement, with at least 3-5 years in AI/ML projects or digital transformation initiatives. Strong knowledge of artificial intelligence and machine learning concepts (supervised, unsupervised learning, NLP, computer vision, etc. Hands-on experience with AI development frameworks and tools (such as Python, R, TensorFlow, scikit-learn, PyTorch) and familiarity with data pipelines and big data technologies (SQL, Spark, Kafka). Solid understanding of AI ethics, data privacy, and security best practices.
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)
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.)
As a Data Scientist / Data Analyst / Data Science Specialist for Adidev Technologies Inc., you will be enhancing and debugging large-scale applications for one of our well-known clients.. Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation-systems, environmental systems and/or agronomic problems.. Practical knowledge and experience with cloud-computing systems and platforms, including the routine deployment of pipelines through Kubernetes. Apply Natural Language Processing and Computer Vision to solve business use cases,. Knowledge of Python libraries (NumPy, Pandas, SciKit-Learn, TensorFlow, PyTorch), Spacy, MongoDB, PostgreSQL, Flask, streamlet and a good knowledge of data pipelines construction
Exploratory Data Analysis (EDA) : Use statistical analysis and data visualization techniques to identify key patterns, trends, and correlations in the data.. Model Selection & Training : Select the appropriate machine learning models based on the problem at hand (e.g., supervised learning, unsupervised learning, deep learning).. Deploy models in a production environment using tools like Flask , FastAPI , Docker , and Kubernetes.. Proficiency in Python, SQL, and experience with libraries such as Pandas , NumPy , Scikit-learn , TensorFlow , and Keras.. Hands-on experience with model deployment tools such as Flask , Docker , Kubernetes , and cloud platforms like AWS , Azure , or Google Cloud.
SOUNDHOUND INC. TURNS SOUND INTO UNDERSTANDING AND ACTIONABLE MEANING. We believe in enabling humans to interact with the things around them in the same way we interact with each other: by speaking naturally to mobile phones, cars, TVs, music speakers, coffee machines, and every other part of the emerging 'connected' world.. This is an opportunity to work on the most challenging data science problems, build large scale distributed machine learning systems from the ground up, and use cutting edge technologies like Spark, Kafka, and Tensorflow.. Build machine learning models for analysis of queries using NLP, Deep Learning. Experience in one or more of the following areas: classification systems, ranking systems, recommender systems, predictive modeling, and/or artificial intelligence. Experience with Deep Learning / Neural Network frameworks such as Caffe, Tensorflow, PyTorch, etc.
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.
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.
Come join Intuit as a Staff Machine Learning Engineer!. Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark). Knowledge of machine learning techniques (i.e. classification, regression, and clustering). Understand machine learning principles (training, validation, etc. Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP)
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.
Your Mission is Ours. Lockheed Martin Space is seeking a highly skilled AI/ML Machine Learning Engineer to join a cross-functional team of experts in research, data science, software development, physics, and mathematics.. Bachelor's degree in a STEM discipline (e.g. Electrical Engineering, Computer Science, Computer Engineering, Mathematics, Physics, etc.). Experience in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, or Image Processing. Experience with TensorFlow, PyTorch, Keras, or Scikit-learn. Experience with RF, SAR or EO image processing algorithms/techniques
Provide advanced actuarial and analytical skills to support the development or enhancements of commercial data-solutions at RGA.. Have a strong understanding on tools / techniques their actuarial peers will not have had a formal education in: Applications, risks, transparency, quality assurance & peer review, ethical guidelines Staying abreast of new techniques, but focusing on practical applications Liaising with RGA's Global D&A team for more sophisticated data science applications Contributing to RGA's global analytics community, routinely sharing, maintaining consistency of approach. Spreadsheet skills (Excel/VBA) and database applications (SQL, Snowflake, Oracle,. Advanced predictive modeling skills: Tree-based models + GLMs Cross-Validation, Residuals and model diagnostics Basic Statistical concepts for feature engineering (e.g. percentiles, standardization, correlations, risk ratios / chi-square test, splines, and other non-linear transformations) Pro-active use of insurance expertise & actuarial concepts to feature engineering and model evaluation. Basic machine learning models/concepts (SVM's, GAN's, Neural Networks/Deep Learning, Naive Bayes, NLP), and/or basic statistical concepts for feature engineering for dimensionality reduction such as PCA's, SVD's, and clustering
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
The role will lead and mentor a team of data scientists and analysts responsible for delivering machine learning, artificial intelligence, and advanced analytics projects. Minimum 5 years of experience in data science, machine learning, statistical modeling, or optimization in an industry or research setting. Experience with big data technologies such as Hadoop, Spark, and cloud platforms (AWS, GCP, Azure) is a plus. Familiarity with machine learning frameworks, such as TensorFlow, PyTorch, or scikit-learn. Advanced proficiency in Python, SQL, PySpark, or similar analytic tools.
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.