Data Science & Machine Learning Jobs

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.

A leading proprietary trading firm specializing in quantitative and algorithmic trading across global financial markets is seeking a Software Engineer - AI & Machine Learning to enhance their trading systems and develop state-of-the-art AI-driven models. Collaborate with quant researchers and traders to refine trading strategies using AI insights. or Java. Strong experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn). Machine Learning Expertise: Hands-on experience in deep learning, reinforcement learning, time-series forecasting, and NLP. Big Data & Infrastructure: Experience with distributed computing (Spark, Ray), cloud platforms (AWS, GCP), and database management (SQL, NoSQL).

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.

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.

Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence, and apply new techniques to improve existing processes and models. 5+ years of experience in data science, with a focus on machine learning, predictive modeling, and advanced analytics. Proficiency in programming languages such as Python, R, and SQL, with experience in libraries and frameworks like TensorFlow, PyTorch, Scikit-learn, and Pandas. Strong understanding of statistical methods, data mining, and machine learning algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning. Experience working with large datasets, cloud computing platforms (e.g., AWS, GCP), and big data technologies (e.g., Spark, Hadoop).

Apply locations USMBurlington - Blue Sky USCA - Promontory E USCA San Jose Innovation Drive time type Full time posted on Posted 30+ Days Ago job requisition id R021549. Job Description: 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.

Job Title: Machine Learning Engineer. Responsibilities The machine learning engineer will be able to deploy machine learning models to production using AWS. The machine learning engineer will be comfortable with accessing data from a variety of sources and transforming it into a usable form.. AWS Developer Certification, DevOps Engineer. General ability in NLP, time-series-analysis, computer vision with a specialization in at least one, verified through the use of public repository projects/published papers. Experience with deep learning frameworks including TensorFlow, PyTorch, MXNet and bringing deep learning models to production at scale

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 looking for an experienced Senior Lead Data Scientist / ML Engineer with a strong blend of pre-sales expertise, team leadership, and technical proficiency across classical machine learning, deep learning, and generative AI. You will engage in high-level client discussions, drive technical sales strategies, and lead a team to design and implement cutting-edge ML solutions.. Pre-Sales & Client Engagement Collaborate with the sales and business development teams to identify client needs and formulate AI/ML solutions.. Deep Learning & Generative AI Develop and maintain deep learning models using frameworks such as TensorFlow or PyTorch for tasks like computer vision, NLP, or recommendation systems.. Deep Learning Hands-on expertise with frameworks (e.g., TensorFlow, PyTorch) for CNNs, RNNs, transformer architectures, etc.. MLOps Familiarity with CI/CD pipelines, Docker/Kubernetes, and cloud platforms (AWS, Azure, GCP).

Job Title: Machine Learning Engineer. Responsibilities The machine learning engineer will be able to deploy machine learning models to production using AWS. The machine learning engineer will be comfortable with accessing data from a variety of sources and transforming it into a usable form.. AWS Developer Certification DevOps Engineer. General ability in NLP, time-series-analysis, computer vision with a specialization in at least one, verified through the use of public repository projects/published papers. Experience with deep learning frameworks including TensorFlow, PyTorch, MXNet and bringing deep learning models to production at scale

Job Title: AI Architect & Machine Learning Developer for Cloud Native AI Solutions. Design scalable infrastructure using containers (Docker, Kubernetes) and cloud platforms (AWS, GCP, or Azure).. Collaborate with cross-functional teams (Data Science, DevOps, Product) to deliver impactful AI solutions.. Proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and programming languages like Python or Golang.. Familiarity with big data (Spark, Hadoop, Kafka) and both SQL/NoSQL databases.

Job Title: Machine Learning Engineer. Strong knowledge of statistical data analysis and machine learning techniques (e.g. Bayesian, regression, classification, clustering, time series, deep learning).. Experience Deep learning (Image analytics) using TensorFlow, PyTorch, Keras.. Experience working in NLP (Documents/Topic Modelling/Contracts/Chatbots).. Programming experience in Python, PySpark, Java, SQL & Bash scripting.

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).

Hands-on experience with popular machine learning libraries and frameworks, (e.g., TensorFlow, PyTorch, Scikit-learn, Keras, SciPy, etc. Experience with Apache Spark, Apache Streaming, and Jupyter Hub+ Event Reporter to introduce ML and pattern recognition to discover hidden insights.. Experience with containerization (e.g., Docker, Kubernetes, Rancher, etc. Working knowledge with cloud computing platforms, (e.g., AWS, Azure, or Google Cloud, etc. Familiarity with big data technologies, (e.g., Elastic Search, Apache Hadoop, Spark, Kafka, etc

We are building a digital native power company operating at the intersection of grid variability, market volatility, economic optimization, commercial structuring, and risk management, across the end-to-end power value chain. 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 ideal candidate will possess deep expertise in machine learning and natural language processing (NLP), along with strong software development skills.. Develop and implement deep learning models using frameworks such as PyTorch, TensorFlow, or Keras.. Build and optimize Natural Language Processing (NLP) solutions for various business applications.. Strong expertise in deep learning frameworks such as PyTorch, TensorFlow, and Keras.. Hands-on experience in Natural Language Processing (NLP).

Exploring research in the data science field and utilizing open source research results for the improvement of customer facing products. Working with terradata, hive, spark, tableau, postgres, other big data systems with Verizon grid computing infrastructure. Experience in one or more languages like Java, R, matlab, Python - generators, iterators, comprehensions, Numpy, Pandas, matplotlib, sklearn, keras, tensorflow, pytorch. Experience with Machine Learning, Statistics and Probability, NLP, Deep Learning especially experience in recommendation systems, conversational systems, information retrieval, computer vision, regression modeling. Knowledge of big data tools (SQL, Spark, and Splunk) ie.

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.. Recommended Skill Set List NetOps Data Engineer/Scientist Classic ML and Statistical Analysis Focused Skills. Deep Learning: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and experience with deep learning frameworks.. Programming & Scripting Skills: Python: Proficient in data manipulation (Pandas, NumPy), data visualization (Matplotlib, Seaborn, Plotly), and machine learning libraries (Scikit-learn, TensorFlow, PyTorch).. Software Engineering Skills: Programming Languages: Proficiency in Python (essential) and potentially other languages like Java, Scala, or Go.

End-to-end design, development and implementation of powerful algorithms and models for Natural Language Processing (NLP), Embedding Based Retrieval (EBR), semantic search, and machine learning to improve query understanding and retrieval.. What you will bring : 1-3 years (with PhD) or 3-5 years (with Masters) of industrial experience in Computer Science, Artificial Intelligence, Information Retrieval, or a related field, with a focus on NLP or machine learning.. Industrial experience with one or more of the following: classification, regression, NLP, clustering, Deep Learning/Neural Networks, Reinforcement Learning, or related.. Strong background in machine learning, NLP, and related technologies, with experience applying these techniques to large-scale, real world problems.. and experience with machine learning frameworks and tools with preferable experience in big data processing, e.g. Hadoop, SQL, Spark.

Computational Scientist - Artificial Intelligence & Machine Learning. 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 need to use machine learning and AI, understand faculty’s research questions and contribute to finding solutions and developing applications.. Experience with one or more machine learning and deep learning frameworks such as TensorFlow, PyTorch, or Keras.