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.

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.

Desired interdisciplinary skills include big data technologies, ETL, statistics and causal inference, Deep Learning, modeling and simulation.. Strong skills in software prototyping and engineering with expertise in applicable programming and analytics languages (Python, R, Spark/Scala) and various open source machine learning and analytics packages to generate deliverable modules and prototype demonstrations of their work.. Advanced proficiency in Python and Spark/Scala for classical statistical analysis and data modeling, machine learning and ETL processes.. Proficiency in data science modeling – AI, Machine Learning, Deep Learning, Decision Trees, Random Forest, Neural Networks, Supervised/Unsupervised Learning, Forecasting, Predictive Modeling and Clustering.. Deep knowledge of fundamentals of machine learning, data mining and statistical predictive modeling, and extensive experience applying these methods to real world problems.

CRITICAL REQUIREMENT Hands-on experience in building deep learning models (CNN,.. RNN) in a work setting, not just academic projects Hands-on experience in building and deploying machine learning and statistical models using Python, PySpark, Machine Learning libraries, SQL. Experience in computing/programming skills; proficiency in Python, R, and Linux shell script.. Experience with Cloud computing platforms - GCP Experience in productionalizing ML models - building data pipelines, model framework and model deployment.. Desirable: PyTorch, Deep Learning, NLP, reinforcement learning, recommender system, etc.. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernisation.

Hands-on experience with popular machine learning libraries and frameworks, (e.g., TensorFlow, PyTorch, Scikit-learn, Keras, SciPy, etc. Desired Skills: 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

-Develop deep learning models using frameworks like TensorFlow or PyTorch for applications such as image recognition, NLP, and speech processing.. -Optimize AI models for performance, accuracy, and (NLP) -Implement NLP solutions for sentiment analysis, chatbots, document processing, and text summarization.. o Develop AI-driven automation for extracting insights from unstructured data & Infrastructure -Deploy AI models in production using cloud services (AWS, Azure, GCP) or on-premises solutions.. -Strong programming skills in Python (preferred), with experience using TensorFlow, PyTorch, or Scikit-learn.. -Experience working with cloud-based AI services such as AWS SageMaker, Azure AI, or Google Vertex AI.-Understanding of machine learning concepts, feature engineering, and model optimization techniques.

Software Developer required to work on Artificial Intelligence Deep Learning, NLP (Natural Language Processing) and Long Short Term Memory Networks.. Artificial intelligence engineer required to solve real world problems and build intelligent Software applications.. Key skills; Machine Learning ideally in information retrieval.. Topic modelling Document retrieval Recommendation systems Deep learning Big data systems Artificial Intelligence Recommender Systems Python Neural Networks C / C. Pattern Recognition Big Data Tensor Flow Speech Recognition Octave

- Strong project experience in Machine Learning, Big Data, NLP, Deep Learning, RDBMS. - 4-5 years of experience building data pipelines using Python, MLLib, PyTorch, TensorFlow, Numpy/Scipy/Pandas, Spark, Hive. - Proficient in REST API development using Python frameworks (Django, Flask etc.). - Experience in building, deploying and productionizing ML models using MLLib, TensorFlow, PyTorch, Keras, Python Scikit-learn. - Experience in Natural Language Processing (NLP) and Computer Vision

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.

Organization Overview: Digital Core is actively looking for an AI Machine Learning Engineer to spearhead our efforts in the development and deployment of machine learning models that enhance predictive analytics and decision-making across multiple processes and systems in pharma.. Proficient Coding Skills: Expertise in Python, R, or similar programming languages, and familiarity with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.. Additional Preferences: Experience in working with AI technologies and tools, such as natural language processing (NLP), AI generation, machine learning algorithms, deep learning, etc.. Strong understanding of software development methodologies including Agile and Scrum, as well as expertise in version control systems like Git. Experience with containerization and orchestration technologies such as Docker and Kubernetes, and familiarity with CI/CD pipeline setup and management.. Our current groups include: Africa, Middle East, Central Asia Network, African American Network, Chinese Culture Network, Early Career Professionals, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinos at Lilly, PRIDE (LGBTQ + Allies), Veterans Leadership Network, Women’s Network, Working and Living with Disabilities.

Explore the exciting world of Applied AI and Machine Learning with opportunities at Sr. Associate, Vice President, and Executive Director levels at our New York location.. You will be at the forefront of developing scalable tools leveraging machine learning and deep learning models to solve real-world problems related to finance, economics, and operations of JP Morgan.. Job Responsibilities Develop scalable tools leveraging machine learning and deep learning models to solve real-world problems for various problems related to finance, economics and operations of JP Morgan.. Collaborate with all of JPMorgan Chase's lines of businesses, such as Investment Bank, Commercial Bank, and Asset Management.. Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal.

Position Overview As an AI Research Scientist at Autodesk Research, you will be doing fundamental and applied research that will help our customers imagine, design, and make a better world.. We are a team of scientists, researchers, engineers, and designers working together on projects that range from learning-based design systems, computer vision, graphics, robotics, human-computer interaction, sustainability, simulation, manufacturing, architectural design and construction.. As a member of the AI Lab in Autodesk Research you will be an expert in research areas such as artificial intelligence, deep learning, generative AI, machine learning, computer vision, reinforcement learning, information retrieval, and natural language processing.. Familiarity with PyTorch, TensorFlow, JAX or similar frameworks Strong coding abilities in Python and/or C. Preferred Qualifications 2D & 3D Generative AI Reinforcement Learning LLMs and Natural Language Processing Computational geometry and geometric methods (e.g. shape analysis, topology, differential geometry, discrete geometry, functional mapping, geometric deep learning, graph neural networks) Multi-modal deep learning and/or information retrieval Architecture, Construction, Manufacturing, Media & Entertainment or other Autodesk domains #LI-JK3

This critical project comprises a series of improvements to the AirCover for Hosts intake experience, which aims to enhance the quality of reimbursement requests, expedite processing times, and improve the overall guest experience.. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg.. natural language processing, CV, personalization and recommendation).. Strong fluency in Python and SQL with experience with the following: Tensorflow, PyTorch, AWS, Spark, Airflow (or equivalent), Kafka (or equivalent), MLFlow. e.g., GPT, LLaMA, Falcon etc) and experience orchestrating LLMs (prompt tuning, RAG, Finetuning, summarization etc.)

This role focuses on knowledge management, semantic search, image processing, and predictive analytics to support Continued Process Verification (CPV) and Annual Product Quality Review (APQR) programs.. Design efficient search tools using natural language processing (NLP) to enable rapid data retrieval.. Skills and Tools Required:Machine Learning & AI ToolsFrameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face. Libraries: Pandas, NumPy, SciPy, OpenCV (for image processing).. Techniques: NLP, deep learning, computer vision, time-series analysis, reinforcement learning.. Storage: S3, BigQuery, Azure Data Lake. Security: IAM, VPC, Key Management Services for regulated environments.

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

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).

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.

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.