Data Science & Machine Learning Jobs

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

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.

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.

We are seeking a highly motivated, self-starter Machine Learning Engineer to join our AI/ML Engineering team to help achieve this mission.. Expert in multiple Programming/scripting languages, i.e. Unix/Linux Shell Scripting, Python, Java, Scala.. Expertise in Big Data technologies such as Hadoop, Spark, HBase, Kafka.. Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).. Good understanding of machine learning libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, etc.

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.

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.

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.

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.

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

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)

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.

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.

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.

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.

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.

- Formulate end-to-end machine learning models by utilizing ML, NLP techniques to deal with real-world signals generated from privacy products/ incidents/ review areas.. Besides the technical side, you will also get the opportunity to work closely with various XFN teams, including but not limited to engineering, legal, audit, security, public, government relations, and products.. - Solid background in NLP and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods.. Python, R, Java, or C. - Experience with machine learning libraries and deep learning toolkits such as PyTorch, Caffe2, TensorFlow, Keras or Theano.

Understands all phases of the model lifecycle, ensuring that models and associated documentation comply with model validation expectations.. One (1) year of hands-on experience with Big Data technologies such as Hadoop, Hive, Impala, Spark, or Kafka.. Two (2) years of working experience with statistical and predictive modeling concepts and approaches such as machine learning, clustering and classification techniques, and artificial intelligence.. Two (2) years of working programming experience analyzing large, complex, and multi-dimensional datasets using a variety of tools such as SAS, Python, Ruby, R, Matlab, Scala, or Java. Deep understanding of statistical and predictive modeling concepts, machine learning approaches, clustering and classification techniques, and recommendation and optimization algorithms

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

Position Summary: 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.