Data Science & Machine Learning Jobs

The goal of a Machine Learning Engineer at Scale is to bring techniques in the fields of computer vision, deep learning and deep reinforcement learning, or natural language processing into a production environment to improve products and customer experience.. Extensive experience using computer vision, deep learning and deep reinforcement Learning, or natural language processing in a production environment. Strong programing skills in Python or Javascript, experience in Tensorflow or PyTorch. Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization. AWS or GCP) and developing machine learning models in a cloud environment

He will independently and in collaboration with others, research, design, develop, and implement innovative Machine Learning, AI, deep learning, NLP, Cloud, Data Science solutions that will advance NYSE's analytics capabilities across multiple business lines.. 2 or more years of experience in applying AI/ML/ NLP / deep learning / data-driven statistical analysis & modelling solutions to quantitative analysis to financial market data.. Strong Knowledge of the theory and applications of machine learning, AI, deep learning, data science, NLP, text analytics, unstructured data analytics, supervised/unsupervised learning.. Java, R , MATLAB, Scala, and with machine learning and deep learning and Big data frameworks including TensorFlow, Caffe, Spark, Hadoop.. Proven capability in demonstrating successful advanced technology solutions (either prototypes , POCs, well-cited research publications, and/or products) using ML/AI/NLP/data science in one or more domains,

Supermicro® is a Top Tier provider of advanced server, storage, and networking solutions for Data Center, Cloud Computing, Enterprise IT, Hadoop/ Big Data, Hyperscale, HPC and IoT/Embedded customers worldwide.. Implement and optimize algorithms for traditional ML models and LLM, ensuring they meet both performance and reliability criteria. In-depth knowledge of various AI and machine learning techniques, including Traditional Machine Learning, Deep Learning, Natural Language Processing (NLP), and Large Language Models (LLM). Proficient in the following ML, Deep Learning and Generative AI tools and frameworks, including Pytorch, TensorFlow, scikit-learn, Large Language Models (LLM), numpy, and pandas. Experience in BigData platforms and frameworks such as Apache Spark and DataBricks is a plus

Senior Machine Learning Engineer. New opening for a Machine Learning Engineer to lead a growing team within digital, gaming and streaming media.. The team is creative with machine learning, predictive modeling, and deep learning.. Develop advanced AI/LLM data driven pipelines to fuel our analytics platform.. Create and apply advanced machine learning and deep learning solutions to extract insights from diverse structured and unstructured data sources.

Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Experience with machine learning libraries like TensorFlow, Pytorch, Scikit-learn, etc. Familiarity with big data technologies (e.g., Spark, Hadoop) and data visualization tools. A multinational organization with 58 offices in 21 countries and the possibility to work abroad.

AI: Machine Learning, Deep Learning, Scikit, Spark ML, TensorFlow, Keras, NLP (spacy, nltk). AWS SageMaker, Ray Distributed systems / Big Data: Spark, Kafka, Hadoop, HBase, Cassandra. Data Science: Python data analytics ecosystem (Pandas, Scikit, etc). Datastores: Vector stores (MongoDB Atlas, Milvus), NoSQL’s (HBase, Cassandra, MongoDB, Redis), and SQLs (Postgres, MySQL). DevOps / Tools: Docker, Kubernetes, Ansible, Terraform, Linux, git

The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.. As an Executive Director on the Machine Learning Center of Excellence (MLCOE) team, you will be responsible for applying advanced machine learning techniques to a variety of complex tasks.. These tasks include natural language processing, speech analytics, time series, reinforcement learning, and 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 (.. : TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.

Director, Artificial Intelligence and Machine Learning (AI/ML) Quality Oversight Director, Artificial Intelligence and Machine Learning (AI/ML) Quality Oversight United States - Maryland - Frederick, United States - California - Santa Monica, United States - California - Oceanside Quality Regular. Job Description We are seeking a highly experienced Director of Artificial Intelligence and Machine Learning (AI/ML) Quality Oversight to establish and lead the quality assurance strategy for our AI/ML initiatives, specifically within a Good Manufacturing Practice (GMP) environment.. Ensure the quality and reliability of Natural Language Processing (NLP) solutions developed for tasks including sentiment analysis and automation.. PhD in degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field with 8+ years of progressive experience in developing and deploying machine learning models and AI solutions, with a strong emphasis on quality assurance and oversight OR. Hands-on experience with NLP, deep learning frameworks (e.g., TensorFlow, PyTorch), and deploying models in production environments, with a quality-centric approach.

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 the team's model inventory and ensures compliance with USAA model risk policies 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.

We are seeking a high energy, driven, and innovative Director of Data Science & Machine Learning Engineering to join our Data Science CoE in Berkeley Heights, NJ. The Data Science CoE aims to provide solutions but also grow Axtria’s footprint and business in the AI/GenAI & ML Ops Engineering space. Expertise in advanced AI/ML algorithms & their applications, NLP, deep learning frameworks, computer vision. Ability to build scalable models using Python, R-Studio, R Shiny, PySpark, Keras, Tensorflow.. Relevant mastery in Feature Engineering, Feature Selection and Model Validation on Big Data. Familiarity with cloud technology such as AWS / Azure and knowledge of AWS tools such as S3, EMR, EC2, Redshift, Glue; viz tools like Tableau and Power BI.

Has obtained a Ph. D. degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Reinforcement Learning, Mathematics, or relevant technical field.. Experience with deep learning frameworks such as Pytorch or Tensorflow.. Experience building systems based on machine learning, reinforcement learning and/or deep learning methods.. Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, or similar.. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

PhD or Masters in Statistics, Mathematics, Economics or similar degree (PhD preferred but not required). 8+ years of experience in end-to-end statistical analysis, data science, and machine learning lifecycles at an enterprise level. Advanced programming skills in modern languages including Python, R, C#, Java, Scala and Go (Python required). Experience with a range of machine learning-related frameworks and libraries, such as Python scikit-learn, Pandas, StatsModels, Keras, TensorFlow. Nor will QUANTUM TECHNOLOGIES LLC require in a posting or otherwise U.S. citizenship or lawful permanent residency in the U.S. as a condition of employment except as necessary to comply with law, regulation, executive order, or federal, state, or local government contract.

Design, build, and validate machine learning models (classification, regression, NLP, CV, etc. Experience with deep learning frameworks like TensorFlow, Keras, or PyTorch.. Experience with model deployment using Docker, Flask, FastAPI, or similar tools.. Experience with NLP, computer vision, or time-series forecasting.. Familiarity with data engineering workflows and tools (e.g., Airflow, Spark).

Job Title: Big Data Machine Learning Engineer. Hadoop, Machine Learning, Java/Scala/Python, Apache Spark, Predictive Modeling, Industrial IoT Analytics, Data Visualization, Natural Language Processing (NLP). Bachelors in Computer Science or Equivalent- Preferred Masters or PhD in Machine Learning, Data Analytics, Big Data or similar. Machine Learning- production implementation not just building the prototypes. Well versed in the Big Data world; candidate should know at least what each of the followings are and should have extensive experience with some: Hadoop, Pig, Hive, Spark, Lambda Architecture, Apache Storm/Samza, Kafka, MR, Oozie/Airflow/

From strategy to execution, this team delivers integrated, end-to-end support and advisory services covering valuation modeling, cost optimization, restructuring, business design and transformation, infrastructure and real estate, mergers and acquisitions (M&A), and sustainability. Successful candidates will be an expert in using state-of the-art technologies such as computer vision, natural language processing (NLP), time-series analysis, graph neural networks, and other AI/ML subdomains to solve complex business problems across diverse applications in healthcare, diagnostics, hospital operations, and more. Master's or PhD 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 MLOps.. 6+ years of experience in core programming languages and data science packages (Python, Keras, Tensorflow, PyTorch, Pandas, Scikit-learn, Jupyter, etc.)

The Strategic Finance team at OpenAI plays a critical role in shaping the company’s long-term trajectory. We’re looking for a senior Machine Learning Data Scientist to lead our forecasting initiatives. You’ll be one of the founding members of the Forecasting pillar within Strategic Finance Data Science, responsible for building and scaling robust, interpretable, and production-ready forecasting systems. 7+ years of experience in applied data science, with deep hands-on exposure to forecasting, predictive modeling, or marketplace systems. SQL , and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries.

Lead Machine Learning Engineer-. Developing and delivering production code in languages such as Python, Golang, Java, and Scala. Architecting and delivering cloud solutions using Google Cloud and AWS. Recommendation and search algorithms including ALS, Neural Nets, Clustering, personalization techniques, time series, NLP, and image modeling. We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed.

Understands all phases of the model lifecycle, ensuring that models and associated documentation comply with model validation expectations. One (1) year of hands-on experience with Big Data technologies such as Hadoop, Hive, Impala, Spark, or Kafka. Two (2) years of working experience with statistical and predictive modeling concepts and approaches such as machine learning, clustering, and classification techniques, and artificial intelligence. Two (2) years of working programming experience analyzing large, complex, and multi-dimensional datasets using tools such as SAS, Python, R, Matlab, Scala, or Java. Deep understanding of statistical and predictive modeling concepts, machine learning approaches, clustering, classification, and optimization algorithms

As a Principal Research Scientist for Boston Fusion, you will have the opportunity to utilize your knowledge of scientific research and software engineering best practices to contribute and drive research efforts in development of systems core to the success of future Intelligence, Surveillance, and Reconnaissance (ISR) activities.. Investigate fundamental topics in a scientific field such as signal processing, artificial intelligence, machine learning, data science, physics and/or math. Classical applied probabilistic, mathematical, and/or artificial intelligence methods (planning, searching, logic, uncertain knowledge, probabilistic reasoning, natural language processing). Machine learning frameworks (e.g., Scikit-learn, Tensorflow, PyTorch, Keras) and concepts (e.g., supervised, unsupervised, active, and reinforcement learning; neural networks; generative, discriminative models). 6+ years of experience applying technology to US Department of Defense and/or Intelligence Community problems, including but not limited to the following: intelligence analysis, command and control, logistics, ISR, information operations, missile defense, operations in the cyber, space, or maritime domains

The Antitrust Division of the U.S. Department of Justice (DOJ) is responsible for enforcing federal antitrust laws and promoting fair competition in the marketplace.. Apply natural language processing (NLP), computer vision, or other domain-specific algorithms as required by the research.. Utilize cloud platforms and big data technologies (e.g., AWS, Azure, Hadoop, Spark) for efficient data processing and model deployment.. Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI) to present insights effectively.. Strong understanding of big data technologies (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud).