Data Science & Machine Learning Jobs

We are looking for an experienced Senior Lead Data Scientist / ML Engineer with strong pre-sales expertise, leadership abilities, and technical proficiency in machine learning, AI, neural networks, and deep learning technologies.. Work on deep learning models using TensorFlow, PyTorch, and transformer-based architectures.. Programming: Proficient in Python with experience in AI/ML frameworks like TensorFlow and PyTorch.. MLOps & Cloud: Experience in CI/CD pipelines, Docker/Kubernetes, AWS, Azure, or GCP.. Knowledge of DevOps tools (Jenkins, Terraform) for infrastructure automation.

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.. Position Overview : 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

We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team.. The successful candidate will be well-versed in Python programming and have a solid understanding of frameworks like TensorFlow and PyTorch.. Utilize Python and libraries such as TensorFlow , PyTorch , and other relevant tools to develop and deploy models.. Experience: Proven experience working in machine learning and deep learning with hands-on experience in TensorFlow , PyTorch , and other machine learning frameworks.. Familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) is a plus.

Predictive Modeling Platform: Develop an on-demand predictive modeling platform using gRPC. Cloud and Containerization: Utilize Kubernetes for managing Docker containers and various cloud services (AWS, Azure) to solve cloud-native challenges. Frameworks: PyTorch, TensorFlow, Docker. Cloud: Kubernetes, Helm, EKS, Terraform, AWS. Hands-on experience with ML and deep learning libraries (Scikit Learn, Keras, TensorFlow, PyTorch, Theano, DyLib)

As an AI/ML Expert within a DevOps team your main objective is to lead the development, deployment, and optimization of machine learning models and AI solutions.. Working closely with our Manager, Software Development and other team members, you will share your expertise to help the team achieve outstanding results and maintain a competitive edge.. Experience with deep learning, NLP, and reinforcement learning.. Strong knowledge of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn).. and cloud platforms (AWS, Azure, Google Cloud).

The Principal Data Scientist will be a key player in our Data Science team, spearheading AI initiatives, research, and development of algorithmic solutions to construct extensive, personalized recommendations for our omni-channel retail platform.. As the Principal Data Scientist, you'll be responsible for the comprehensive implementation of Machine Learning algorithms and features, guiding a scrum team and working in synergy with a skilled team of Product Managers and Technologists to implement ML/AI solutions at scale.. Proficiency in Programming and Data Analysis : Candidates should have high proficiency in languages such as Java, Python, Pyspark, and experience with machine learning frameworks like Tensorflow and PyTorch.. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting.. Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more.

We are looking for an experienced Senior Lead Data Scientist / ML Engineer with strong pre-sales expertise, leadership abilities, and technical proficiency in machine learning, AI, neural networks, and deep learning technologies.. Classical Machine Learning & Statistical Modeling: Develop models using regression, classification, clustering, and ensemble methods.. Deep Learning & Generative AI: Work on deep learning models using TensorFlow, PyTorch , and transformer-based architectures.. MLOps & Cloud: Experience in CI/CD pipelines, Docker/Kubernetes, AWS, Azure, or GCP.. Knowledge of DevOps tools (Jenkins, Terraform) for infrastructure automation.

We seek candidates with: Bachelor's degree, Master's degree preferred, in data science, computer science, quantitative finance, statistics, or mathematics. Strong knowledge of data visualization tools such as Tableau, Power BI, or Matplotlib.. Preferred Qualifications: Experience with Natural Language Processing (NLP) or Computer Vision.. Knowledge of big data technologies such as Spark, Hadoop, or Databricks.. NRG Energy is committed to a drug and alcohol-free workplace.

Data Scientist/Engineer practitioner to work on designing and developing a network operations AI assistant expert system using generative AI, traditional Machine Learning, and statistical analysis tools and techniques.. Deep Learning: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and experience with deep learning frameworks.. Python: Proficient in data manipulation (Pandas, NumPy), data visualization (Matplotlib, Seaborn, Plotly), and machine learning libraries (Scikit-learn, TensorFlow, PyTorch).. Programming Languages: Proficiency in Python (essential) and potentially other languages like Java, Scala, or Go.. We are a global technology leader that designs, develops and supplies a broad range of semiconductor and infrastructure software solutions.

Our mission is to ensure the FAA remains a global leader in aviation safety, innovation, and efficiency while safeguarding its workforce and mission-critical operations.. in Computer Science, Engineering, Artificial Intelligence, Machine Learning, Data Science, or a related field.. Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, or a related field.. Strong coursework in machine learning, deep learning, algorithms, data structures, probability theory, and statistics.. Proven experience in AI, ML, and Natural Language Processing (NLP) , including model development and deployment.

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.. Experience in time series forecasting, anomaly detection, and natural language processing is a plus.. Experience with Python-based data science and machine learning libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, Keras, and PyTorch.. Proficiency with data visualization tools such as Matplotlib, Seaborn, Plotly, or Tableau.. Experience with deep learning techniques and frameworks such as TensorFlow and PyTorch.

The Fay Group originated in 2008, is a full-scale real estate services company that offers mortgage servicing, property renovations, business purpose lending, insurance, and more to homeowners, investors, and clients nationwide.. Reporting to the SVP, Data Science, this position supports the data science team in developing and implementing statistical models that use machine learning, time-series analysis, Monte-Carlo simulations, and reinforcement learning techniques.. This role is responsible for data mining, predictive modeling, and statistical analysis to help solve complex business problems and improve operational efficiencies.. Strong working knowledge of relevant programming languages, tools, and technologies (e.g., Python, R, Hadoop, Spark, etc.). Equity and Inclusion are embedded into our way of working at Fay. We believe that the best ideas come from having a team that is diverse in backgrounds, experiences, and perspectives.

Department Provost Research Computing CenterAbout the Department The University of Chicago Research Computing Center (RCC), a unit in the Office of Research, provides high-end research computing resources to researchers at the University of Chicago.. These resources include hardware, software, high-level scientific and technical user support, and the education and training required to help researchers make full use of modern HPC technology and local and national supercomputing resources.. The Research Computing Center (RCC) seeks to hire an experienced Computational Scientist – Scientific–AI and Machine Learning to serve as a domain expert in supporting and advising faculty, post-docs, and graduate students on projects in a wide range of research domains.. In this role, the Computational Scientist will support research projects that needs to use machine learning and AI, understand faculty’s research questions and contribute to finding solutions and developing applications.. Technical Skills or Knowledge:Experience with one or more machine learning and deep learning frameworks such as TensorFlow, PyTorch, or Keras.

We are seeking an experienced Senior Data Scientist with over 5 years of experience in Data Science, Machine Learning (ML), and Artificial Intelligence (AI) to join our team in Redwood City, SF Bay Area.. Ensure best practices in ML model validation, interpretability, and performance optimization.. Work with big data technologies and cloud platforms such as AWS, GCP, or Azure to scale AI/ML solutions.. Deep expertise in machine learning algorithms, deep learning, NLP, computer vision, and reinforcement learning.. Hands-on experience with cloud platforms (AWS, GCP, Azure) and big data technologies (Spark, Hadoop, Snowflake).

Provide technical guidance in the fields of NLP, Machine Learning, Statistical Methods. Experience with Client frameworks: Tensorflow, PyTorch, Transfomers, Scikit-learn XGBoost, LSTM, Keras, Pandas, BERT, CNN, RNN, SVMs, k-Nearest Neighbors, Linear/Logistic Regression and Classification, Ensemble Methods, Graphical Models, Clustering, Tesseract. Hadoop, Spark, HDFS, MapReduce, YARN, Scala, MapReduce, Pyspark. IBM MQ Series Education Level Master's Degree Work Location On-site (Government / AFS Site): Woodlawn, MD On-site. Clearance Required : ☒ Public Trust ☐ Secret ☐ Top Secret ☐ TS/SCI ☐ None (Background check validation per MSA) ☐ Other

The Data Scientist, Machine Learning Engineer will also be responsible for continuously monitoring model performance, making adjustments as needed to improve accuracy and efficiency.. , AWS, Azure, Google Cloud) for computing resources, data storage, and model hosting.. Advanced proficiency with data mining, mathematics, and statistical analysis Advanced pattern recognition and predictive modeling experience Knowledge of machine learning frameworks (scikit-learn, TensorFlow, PyTorch) and data visualization tools (.. Minimum 5 years’ experience in data science, with experience in predictive modeling and unsupervised machine learning.. Experience in programming languages (such as Python or C#) and familiarity with SQL required Experience deploying data/code to cloud computing platforms (such as AWS, Azure, Google Cloud) and their data analytics services preferred

You'll be given an introduction to JAVA and OOPS and its related technologies like SERVLET, JSP, JQUERY, HIBERNATE, SPRING, JAVASCRIPT, and MICROSERVICES. Introduction to Java, Oops Concepts Multi-threading, Exception Handling, Java API's JSP, Servlets How to deploy a web application jQuery , AJAX, JavaScript, JSON, Jenkins, GitHub, Spring MVC, Spring Core, Spring Boot, Rest Webservices, Hibernate, Spring Security, Microservices.. DATA STRUCTURES AND ALGORITHMS In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.. Our data science track covers most of the below: Topics and content might change based on the market requirements Python NumPy and Pandas Matplotlib data visualization Difference between Machine learning, Artificial Intelligence and Deep learning Data Manipulation: Cleansing-Munging Data Analysis.. Statistics Tableau and Power BI PL/SQL and databases both SQL and NoSQL Data structures and algorithms Artificial Intelligence and Machine learning Machine learning Algorithms Decision Tree and Random Forest Algorithms Naïve Bayes and KNN Algorithm Support Vector Machine (SVM) Statistics Random variables, Zscore, Hypothesis testing, Expected Value Predictive Modeling : Different kind of Business problems and different phases of Predictive modeling and Popular Modeling Algorithms.. Time series Analysis Different algorithms like Decision Tree and Random Forest algorithms, Support Vector Machine Algorithm Deep Learning and Computer vision Neural Networks, Tensor Flow and Keras Natural language processing (NLP) and Text mining Market Basket Analysis NLP with Python, Sentiment analysis Linear regression, Scikit Learn, Confusion matrix, Decision tree, Ensemble approach Random forest, Cross validation.

5 plus years of hands-on experience with Python and/or R programming and statistical packages, and ML libraries such as scikit-learn, TensorFlow, PyTorch, etc.. 3 plus years of experience in building use cases / solutions especially around AI/ML cognitive services, based on Cloud infrastructure and services such as Azure cloud platforms and On- premise environmentsExpertise with SQL, noSQL, Python, R, Javascript programming languages and big data environments (such as Splunk, Hadoop, Spark, Flink, Stream Analytics, Kafka, Docker, Kubernetes etc.). Expertise with scaling pilot machine learning solutions to a large scale production environmentExpertise with visualization tools such as PowerBI, D3JS etc.. Desired :- Bachelor or Masters degree in highly quantitative field (computer science, or electrical engineering, mathematics, statistics) or equivalent domain specific experience in lieu of a degree.. Proficient in machine learning data workflows, data collection methodologies, and data analysis.

Position Summary Deep Learning Data Science ManagerSFL Scientific, a Deloitte Business practice brings together several key capabilities to architect integrated programs that transform our clients' businesses, including Strategic Growth Transformation, Transformation Strategy & Design, Technology Strategy & Business Transformation, and AI & Data Strategy.. We are hiring a Data Science Manager to support the technical design, development, and deployment of novel AI solutions across healthcare, life sciences, manufacturing, energy, and other sectors.. Mentor, motivate and coach junior data scientists on technical best practices and inspire professional developmentThe TeamSFL Scientific, a Deloitte Business, is a data science professional services practice focused on strategy, technology, and solving business challenges with Artificial Intelligence (AI).. Qualifications:Master’s or Ph. D degree in a relevant STEM field (Data Science, Computer Science, Engineering, Mathematics, Physics, etc.). 6+ years of experience working in data science, data engineering, software engineering, or MLOps6+ years of experience in AI/ML algorithm development workflow and data analysis in the major data modalities from NLP, time-series analysis, computer vision, to graph models6+ years of experience in core programming languages and data science packages (Python, Keras, Tensorflow, PyTorch, Pandas, Scikit-learn, Jupyter, etc.)

The Product Marketing Customer Analytics team is seeking a Machine Learning Engineer with deep technical experience in predictive analytics and analytic engineering.. Manage ML projects through all phases, including data quality, algorithm/feature development, predictive modeling, visualization, and deployment and maintenance.. Graduate degree required in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field.. Comfortable with advanced deep learning frameworks (Tensorflow, PyTorch) and adept at designing and scaling ML platforms that include feature stores, automated retraining pipelines and CI/CD integration.. Solid technical database and data modeling knowledge (Oracle, Hadoop, SnowFlake), and experience optimizing SQL queries on large dataset for performance-critical analytics.