Data Science & Machine Learning Jobs

Strong proficiency in Python, R (must-have) with experience in libraries such as Scikit-learn, and TensorFlow/PyTorch. Expertise in supervised and unsupervised machine learning techniques, including regression, classification, clustering, anomaly detection, and deep learning. Hands-on experience with Dataiku for end-to-end data science and data visualization tools (e.g., Looker, Power BI). Experience within Hi-Tech / Telecommunication specific industry/domain such as Supply Chain, Accounting, Sales etc. Non-Sales employees may be eligible for a discretionary incentive bonus, while Sales employees may be eligible for a sales commission.

We are seeking a Principal Applied Machine Learning Engineer to be the foundational hire responsible for establishing the company's machine learning capabilities.. Develop models for classification, regression, NLP, and LLM-based use cases, aligned to critical business needs in operations, payments, and product workflows.. Deep hands-on experience with AWS ML services, especially SageMaker and Bedrock.. Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.. Familiarity with big data tools like Spark, Kafka, or Hadoop.

We provide extensive expertise in Synthetic Aperture Radar (SAR) image processing algorithm development, computer vision, modeling and analysis of radar systems & pattern recognition technologies. We are looking to add an experienced Machine Learning Engineer to our esteemed team who can develop cutting edge Deep Learning technologies in the domains of Computer Vision, Synthetic Aperture Radar (SAR), and Geospatial Exploitation. The Machine Learning Engineer - Deep Neural Networks will develop, train, and deploy machine learning algorithms and neural networks to solve challenging real-world problems. 1-5+ years of experience in Machine Learning (ML), Data Science, and Artificial Intelligence (AI), ideally with deep learning experience.. Ideally you've worked with most or some of the following: Computer Vision, Radar, SAR, Lidar, Matlab, Deep Neural Networks, pandas, NumPy, Keras, PyTorch, scikit-learn, and open source frameworks

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.

As a Machine Learning Scientist - Natural Language Processing (NLP) - Senior Associate, you will have the opportunity to apply sophisticated machine learning methods to complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems.. Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems. Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods. Extensive experience with machine learning and deep learning toolkits (.. Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal

Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems. Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods. Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). Strong publication record in Natural Language Processing, Machine Learning, Deep Learning or Reinforcement Learning at major conferences or journals. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

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 Experimental Design. Conducts exploratory data analysis, predictive analytics, and prescriptive analytics leveraging Big Data, NLP, and chatbot technologies such as Elasticsearch and Solr. Promotes the use of ML and DL frameworks like TensorFlow, Keras, MXNET, and H2O. Expertise in predictive modeling, training, and evaluating ML algorithms using Python and frameworks like scikit-learn, TensorFlow, PyTorch, Keras, especially in Conversational AI and Search applications. Experience designing NLP and NLU solutions such as Chatbots, Information Retrieval, NER, Summarization, and Text classification using NLP, ML, DL, and embedding techniques like LSTM and BERT.

Its patent-pending approach uniquely combines advances in data science and technology (AI, machine learning, cloud computing) to transform risk management.. Deep understanding of machine learning/statistical algorithms such as time series analysis and outlier detection, neural networks/deep learning, boosting and reinforcement learning.. Expertise in an analytical language (Python, R, or the equivalent), and experience with databases (GCP, SQL, or the equivalent). Experience in building NLP solutions and/or GEN AI are strongly preferred. 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy

Design, build, and train machine learning models using appropriate algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning).. Use various machine learning and AI frameworks (TensorFlow, PyTorch, Scikit-learn, Keras, etc.). Develop and optimize algorithms for specific use cases like image recognition, natural language processing (NLP), speech recognition, or recommendation systems.. Strong programming skills in Python, R, or similar languages for machine learning and data analysis.. Deep knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost.

Role: Data Scientist - AI Strategy & Implementation(This role is open to US Citizens, Green Card holders, GC-EAD only.. 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. Knowledge of Python libraries (NumPy, Pandas, SciKit-Learn, TensorFlow, PyTorch), Spacy, MongoDB, PostgreSQL, Flask, streamlet and a good knowledge of data pipelines constructionMust have

Design, develop, and implement machine learning and deep learning models to analyze data from wearable sensors (e.g., activity trackers, continuous glucose monitors) and echocardiography images.. Ph. D. degree in Data Science, Biostatistics, Computer Science, Biomedical Engineering, or a related quantitative field.. Experience in analyzing medical imaging data, particularly echocardiography, including image processing, feature extraction, and the application of computer vision techniques.. Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is a plus.. Signal Processing: Filtering, noise reduction, feature extraction from raw sensor data (e.g., frequency domain analysis).

The Data and Artificial Intelligence Platform (DAP) group is a key component of Visa’s Technology organization that provides the enabling technology and processes to handle Visa’s data assets and deliver valuable information, products, and services to customers.. Experience with data science tools and technologies (e.g., TensorFlow, PyTorch, scikit-learn). Expert knowledge in Deep Learning techniques and LLM (Large Language Model).. Experience working with Airflow, GitHub, ML flow for building and maintaining ETL pipeline.. Proficient in advanced data mining and statistical modeling techniques, including Predictive modeling (e.g., binomial, and multinomial regression, ANOVA), Classification techniques (e.g., Clustering, Principal Component Analysis, factor analysis), Decision Tree techniques (e.g., CART, CHAID).

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)

Design, develop, and implement machine learning and deep learning models to analyze data from wearable sensors (e.g., activity trackers, continuous glucose monitors) and echocardiography images.. Ph. D. degree in Data Science, Biostatistics, Computer Science, Biomedical Engineering, or. Experience in analyzing medical imaging data, particularly echocardiography, including image processing, feature extraction, and the application of computer vision techniques.. Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is a plus.. Alexion provides reasonable accommodations to meet the needs of candidates and employees.

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.

Strong development experience with any of the following software languages: Python, R, SQL, or Scala.. Professional knowledge of Machine Learning Algorithms: NLP, Neural networks, deep learning, Naïve Bayes, regression, random forest, clustering, and text mining.. Strong experience with common ML libraries and frameworks (ex: PyTorch, Tensorflow, Scikit-Learn, MLflow, Sagemaker, Kubeflow, Spark MLlib, HuggingFace, or LangChain).. Proficiency with data platforms such as Databricks, Snowflake, BigQuery, Spark in any flavor, HIVE, Hadoop, Cloudera, or RedShift (we use Databricks).. Our People First Culture celebrates diversity, equity and inclusion not simply because it’s the right thing to do, but also because it’s the key to our success.

Discover data sources, access, import, clean, and prepare them for machine learning.. Proficiency with data science tools and frameworks (Python, Scikit-learn, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).. Fundamental knowledge of machine learning techniques (classification, regression, clustering).. Experience with GPU acceleration (CUDA, cuDNN).. Experience integrating applications with cloud platforms (AWS, GCP).

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 Experimental Design.. Conducts exploratory data analysis, predictive analytics, and prescriptive analytics leveraging Big Data, NLP, and chatbot technologies such as Elasticsearch and Solr. Promotes the use of ML and DL frameworks like TensorFlow, Keras, MXNET, and H2O.. Expertise in predictive modeling, training, and evaluating ML algorithms using Python and frameworks like scikit-learn, TensorFlow, PyTorch, Keras, especially in Conversational AI and Search applications.. Experience designing NLP and NLU solutions such as Chatbots, Information Retrieval, NER, Summarization, and Text classification using NLP, ML, DL, and embedding techniques like LSTM and BERT.