Data Science & Machine Learning Jobs

This role is perfect for someone passionate about the intersection of computational science, data analysis, and business consulting.. Strong background in statistical and machine learning techniques, with experience using frameworks such as statsmodels, scikit-learn, PyTorch, or TensorFlow.. Knowledge of advanced methods, including Logistic Regression, Time Series Analysis, GLMs, Predictive Modeling, Decision Trees, Random Forests, Neural Networks, and Gradient-Boosted Trees.. Hands-on experience with cloud-based machine learning platforms like AWS SageMaker or Azure ML to scale computing resources.. Experience working with Snowflake, Netezza, or Microsoft SQL environments.

Hands-on experience using statistical or machine learning frameworks to solve a variety of real-world problems (e.g., statsmodels, scikit-learn, PyTorch, TensorFlow, etc). Knowledge of methods like Logistic Regression, Time Series Analysis, GLMs, Mixed Modeling, Multivariate Statistics, Predictive Modeling, Decision Trees, Gradient-Boosted Trees, Random Forests, and Neural Networks. Hands-on experience with cloud-based machine learning platforms (e.g., AWS SageMaker or Azure ML) to leverage scalable computing resources and tools. Experience with the Snowflake, Netezza, or Microsoft SQL environment.. All qualified applicants will be considered regardless of race, color, sex, gender identity, gender expressions, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other protected basis under the law.

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.

Job Summary: We seek a Senior ML Scientist to drive innovation in AI ML-based dynamic pricing algorithms and personalized offer experiences. 8+ years in machine learning, 5+ years in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence. Expertise in classical ML techniques (e.g., Classification, Clustering, Regression) using algorithms like XGBoost, Random Forest, SVM, and KMeans, with hands-on experience in RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization. Experience with ML frameworks and libraries such as scikit-learn, TensorFlow, and PyTorch.. Reinforcement Learning Expertise: Develop and apply RL techniques, including Contextual Bandits, Q-learning, SARSA, and concepts like Thompson Sampling and Bayesian Optimization, to solve pricing and optimization challenges.

BT-69 – Machine Learning Engineer. The organization seeks to develop and adapt Large Language Models (LLM) and Natural Language Processing (NLP) methodologies using artificial intelligence and machine learning (AI/ML) frameworks and programming languages to create new capabilities to improve analytic workflows and address key intelligence questions.. Machine Learning, AI, Deep Learning (TensorFlow, PyTorch), Text, (Classification, NLP, Topic and Language Modeling, Sentiment Analysis, Information Retrieval), Recommender Systems and Personalization, Threat Detection, Computer Vision, Data Mining, Statistics, or similar.. Demonstrated professional or academic experience with deep learning frameworks such as PyTorch, Tensorflow, or Keras.. Demonstrated experience with front-end web development frameworks such as Flask.

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

Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and cloud platforms (AWS, GCP, Azure). Proficiency in MLOps tools (Kubeflow, MLflow, Airflow, Docker, Kubernetes). Deep understanding of distributed computing, big data technologies (Spark, Hadoop), and scalable data pipelines. Experience with NLP, deep learning, reinforcement learning, generative AI. Implement microservices architecture to build scalable and resilient software solutions and use Cloud platforms like AWS, Azure to deploy and run software applications

We are looking for a Machine Learning Scientist / Engineer who will be converting abstract, high-level goals into concrete, measurable requirements. Deep learning, computer vision, topic modeling, graph algorithms are pluses.. Strong programming skills and hands-on experience with machine learning tools and libraries such as PyTorch, TensorFlow, Scikit-learn; programming skills in Scala, Python, Java, or C. Knowledge of Spark, Apache Hadoop, Solr/Lucene, Cassandra, and related big data technologies.. Masters or PhD degree in Machine Learning, Computer Science, Electrical/Computer Engineering, or related fields.

The Harris School of Public Policy seeks applicants for a one-year Predoctoral Scholar - Machine Learning Data Scientist position.. Design and implement NLP algorithms and techniques for text preprocessing, feature extraction, sentiment analysis, semantic role labeling, and document classification.. Proficiency in Stata; ability to use other statistical software packages (e.g., R, Python, or BASH).. Experience with or proficiency in R, Python, and Bash scripting.. Experience implementing Neural Networks using Tensorflow, PyTorch, Scikit-Learn, OpenCV, deep learning, and other artificial intelligence techniques.

Job DescriptionSr Data Scientist with GenAI & NLP - Hybrid on siteMinimum Qualifications:CLIENT PREFERS A PHD - Work or educational background in one or more of the following areas: machine learning, computational linguistics, deep learning, ratification intelligence, data science and/or data analytic, generative AI, symbolic AI, causal AI, operations research, computer science, Mathematics, business analytics, or knowledge management.. Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI development architectures with Human-in-the-Loop (HITLDemonstrated experience with SQL and any relational database technologies, such as Oracle, PostgreSQL, MySQL, RDS, Redshift, Hadoop EMR, Hive, etc.. Very comfortable working with ambiguity (e.g. imperfect data, loosely defined concepts, ideas, or goals)Looking for "hands on" Data Scientist with Fraud detection and time series analysis At least a Master's degree (PhD highly preferred) in Computer Science or any field related to AI.Experience working with big data in AWS and using libraries such as PySpark.. Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI development architectures with Human-in-the-Loop (HITL Demonstrated experience with SQL and any relational database technologies, such as Oracle, PostgreSQL, MySQL, RDS, Redshift, Hadoop EMR, Hive, etc.. 3+ years of relevant experience in building large scale machine learning or deep learning models and/or systems 1+ year of experience specifically with deep learning (e.g., CNN, RNN, LSTM) 1+ year of experience building NLP and NLG tools.

Sr Data Scientist with GenAI & NLP - Hybrid on siteMinimum Qualifications:CLIENT PREFERS A PHD - Work or educational background in one or more of the following areas: machine learning, computational linguistics, deep learning, ratification intelligence, data science and/or data analytic, generative AI, symbolic AI, causal AI, operations research, computer science, Mathematics, business analytics, or knowledge management.. Experience with LoRA, LangChain, RAG, LLM Fine Tuning and PEFT, Knowledge Graphs.. Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI development architectures with Human-in-the-Loop (HITLDemonstrated experience with SQL and any relational database technologies, such as Oracle, PostgreSQL, MySQL, RDS, Redshift, Hadoop EMR, Hive, etc.. Very comfortable working with ambiguity (e.g. imperfect data, loosely defined concepts, ideas, or goals)Looking for "hands on" Data Scientist with Fraud detection and time series analysis At least a Master's degree (PhD highly preferred) in Computer Science or any field related to AI.Experience working with big data in AWS and using libraries such as PySpark.. Knowledge and implementation experience with NLP techniques (topic modeling, bag of words, text classification, TF/IDF, Sentiment analysis) and NLP technologies such as Python NLTK, or Spacy or comparable technologiesKnowledge and implementation experience with statistical and machine learning models (regression, classification, clustering, graph models, etc.)

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.

Hands-on experience using statistical or machine learning frameworks to solve a variety of real-world problems (e.g., statsmodels, scikit-learn, PyTorch, TensorFlow, etc. Knowledge of methods like Logistic Regression, Time Series Analysis, GLMs, Mixed Modeling, Multivariate Statistics, Predictive Modeling, Decision Trees, Gradient-Boosted Trees, Random Forests, and Neural Networks.. Hands-on experience with cloud-based machine learning platforms (e.g., AWS SageMaker or Azure ML) to leverage scalable computing resources and tools.. Experience with the Snowflake, Netezza, or Microsoft SQL environment. Our mission is to shape the future of work by aligning the right mix of people, process, technology, and innovation to efficiently meet our clients' business objectives.

Develop and implement machine learning models: Utilizing various techniques such as regression, classification, clustering, deep learning, and natural language processing.. Strong technical skills in programming languages such as Python, R, or SQL, and experience with machine learning frameworks such as scikit-learn or TensorFlow.. Experience with data visualization tools such as Tableau, Power BI, or D3.. Experience with deep learning frameworks such as TensorFlow or PyTorch.. Certifications in data science, such as Certified Data Scientist (CDS) or Certified Analytics Professional (CAP).

Minimum 5 years of experience in Python, Artificial Intelligence, Neural Networks, Natural Language Processing, Computer Vision, machine learning, and data science.. We are looking for an experienced Senior Lead Data Scientist / ML Engineer with a strong blend of pre-sales , team leadership , and technical proficiency across classical machine learning, deep learning, and generative AI.. Establish best practices in solution design, code reviews, model validation, and deployment.. Develop and maintain deep learning models using frameworks like TensorFlow or PyTorch for tasks like computer vision, NLP, or recommendation systems.. Expertise in classical ML algorithms, deep learning frameworks (TensorFlow, PyTorch), and generative AI models.

At Zoom, we help people stay connected and enhance collaboration through products like Zoom Contact Center, Zoom Phone, Zoom Events, Zoom Apps, Zoom Rooms, and Zoom Webinar. Have hands-on experience developing AI/ML solutions, including LLMs, deep learning, NLP, and reinforcement learning, using frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn. Be proficient in designing ETL pipelines for large datasets; familiar with Airflow, Hadoop, Spark; experienced with RDBMS (Oracle, MySQL, MS SQL) and NoSQL (MongoDB, MariaDB). Have knowledge of Kubernetes, containers, and CI/CD for AI/ML; experience with AWS, GCP, or Azure AI/ML services; skilled in debugging and optimizing large-scale data performance. Our structured hybrid approach is centered around our offices and remote work environments.

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.

Job Title : AI/Machine Learning Engineer. Design and develop machine learning models and AI algorithms for a variety of use cases (e.g., recommendation systems, NLP, computer vision, predictive analytics).. Strong programming skills in Python (using libraries such as scikit-learn, TensorFlow, PyTorch).. Hands-on experience with machine learning, deep learning, and statistical modeling.. Cloud experience (AWS, Azure, or GCP) in building and deploying ML pipelines.

Utilize frameworks such as Scikit Learn, TensorFlow, PyTorch, Vertex AI, and Azure AI Studio. Experience implementing multiple machine learning techniques such as supervised, unsupervised, reinforcement learning, Deep learning, NLP. Solid technical writing skills and must have published research papers in leading academic journals on topics related to Artificial Intelligence & Machine Learning. Proven sound understanding of common NLP tasks such as text classification, entity recognition, entity extraction, and question answering. We believe everyone–of every race, gender, sexuality, age, location and income–deserves the opportunity to live their healthiest life.

Visa Predictive Modeling (VPM) team develops and maintains predictive machine learning models to primarily support Visa Risk and Identity Solutions.. Using VisaNet data and leveraging Machine Learning (ML) and Artificial Intelligence (AI), our model scores help Visa clients all over the world for fraud defense, identity verification, smart marketing, etc.. Managing model risks in line with Visa Model Risk Management requirements.. Strong background in two or more of the following areas: machine learning, deep learning, AI algorithms, statistical learning, computations, scalable systems (e.g. Spark, Hadoop), large scale data analysis, software engineering (automation). Experience with advanced and emerging technologies and tools in big data and data science (e.g., Python, Spark, TensorFlow, PyTorch, H2O, Dask, etc.)