Data Science & Machine Learning Jobs

Our Digital Data Science Department is focused on building predictive solutions to support our mobile app and online banking operations. Combine knowledge of data science techniques with industry research on solutions in the mobile banking sector, including but not limited to logistic regression, random forest, XGBoost, neural networks, NLP, k-means clustering, ARIMA, and prophet forecasting.. Programming abilities in SQL, Python, R, or similar languages in data exploration, data preparation, modeling, prediction, and statistical analysis.. Ability to learn and use data and cloud environments such as Azure and Databricks.. Strong knowledge of statistics and machine learning techniques and tools including logistic regression, XGBoost, neural networks, NLP, and clustering

Join to apply for the Staff Machine Learning Engineer - VC Backed Startups role at SignalFire.. Strong Python skills with frameworks like TensorFlow, PyTorch, or JAX. Experience with big data tools (Apache Spark, Kafka, Hadoop) and MLOps platforms.. Familiarity with cloud environments (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).. Technologies You Might Work With: Python, TensorFlow, PyTorch, JAX, Kubernetes, Docker, MLflow, Kubeflow, FastAPI, SQL, NoSQL, Airflow, Spark, Kafka, Hadoop, AWS (SageMaker, Lambda, S3), GCP (Vertex AI, BigQuery), Azure (ML Studio, Synapse).

Analytical needs can include: data aggregation / creation, data cleaning / manipulation, commercial data science (e.g., geospatial, machine learning, predictive modelling, NLP, GenAI etc. A minimum of 2 years of experience in applied data science with a solid foundation in machine learning, statistical modeling, and analysis is required for a Data Scientist.. Strong knowledge, experience, and fluency in a wide variety of tools including Python with data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch), Spark, SQL; familiarity with Alteryx and Tableau preferred.. Technical understanding of machine learning algorithms; experience with deriving insights by performing data science techniques including classification models, clustering analysis, time-series modeling, NLP; technical knowledge of optimization is a plus.. (e.g., Sagemaker, Azure ML, Kubernetes, Airflow)

Lead impact tracking for Analytics and AI within the Segments and partner with the Enterprise Data Strategy Director.. Recognized expert in multiple data science techniques including experimental design and predictive modeling.. Demonstrated Expertise with at least one Data Science environment (R/RStudio, Python, SAS) and at least one database architecture (SQL, NoSQL). Strong experience with machine learning environments (e.g., TensorFlow, scikit-learn, caret).. Business Performance Improvement - Director (Finance Transformation)

SOUNDHOUND INC. TURNS SOUND INTO UNDERSTANDING AND ACTIONABLE MEANING. We believe in enabling humans to interact with the things around them in the same way we interact with each other: by speaking naturally to mobile phones, cars, TVs, music speakers, coffee machines, and every other part of the emerging 'connected' world. This is an opportunity to work on the most challenging data science problems, build large scale distributed machine learning systems from the ground up, and use cutting edge technologies like Spark, Kafka, and Tensorflow. Build machine learning models for analysis of queries using NLP, Deep Learning.. Experience in one or more of the following areas: classification systems, ranking systems, recommender systems, predictive modeling, and/or artificial intelligence.. Experience with Deep Learning / Neural Network frameworks such as Caffe, Tensorflow, PyTorch, etc.

Come join Intuit as a Staff Machine Learning Engineer!. Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).. Knowledge of machine learning techniques (i.e. classification, regression, and clustering).. Understand machine learning principles (training, validation, etc.). Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP)

Deep understanding of AI/ML concepts with expert knowledge in Large Language Models, Deep Learning, Neural Networks, NLP. MSc or PhD in a quantitative field, e.g., Computer Science, NLP, AI, Machine Learning.. 5+ years of experience in Deep Learning, Neural Networks, Generative AI, Knowledge graph, NLP and relevant frameworks.. Experience with common analytics and data science frameworks: PyTorch, NumPy, sci-kit-learn, pandas, Keras, TensorFlow, etc.. Job Segment: Research Engineer, Training, Computer Science, Compliance, Engineering, Research, Education, Technology, Legal

Analytical needs can include: data aggregation / creation, data cleaning / manipulation, commercial data science (e.g., geospatial, machine learning, predictive modelling, NLP, GenAI etc.. Strong knowledge, experience, and fluency in a wide variety of tools including Python with data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch), Spark, SQL; familiarity with Alteryx and Tableau preferred. Technical understanding of machine learning algorithms; experience with deriving insights by performing data science techniques including classification models, clustering analysis, time-series modeling, NLP; technical knowledge of optimization is a plus. e.g., Sagemaker, Azure ML, Kubernetes, Airflow). AI Training for Data Science (Freelance, Remote)

Research, design, and implement state-of-the-art AI methodologies and techniques to enhance predictive modeling and optimization. Strong expertise in machine learning, deep learning, and statistical modeling techniques, with hands-on experience in developing and deploying AI models. Proficiency in programming languages such as Python and R, along with experience in using AI libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Solid understanding of data preprocessing, feature engineering, and data visualization techniques. Experience with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark) for handling large-scale datasets.

This opportunity is with one of our global high-tech clients, a leader in AI-driven technology and digital transformation. Our client is at the forefront of artificial intelligence, leveraging cutting-edge data science and machine learning to solve complex challenges. Expertise in Python, R, SQL, and data science libraries (Pandas, NumPy, SciPy, Scikit-learn). Experience with deep learning frameworks (TensorFlow, PyTorch). Proficiency in big data technologies (Spark, Hadoop, Databricks, Snowflake).

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 Scale's 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

We are looking for a Senior Machine Learning Engineer to join the growing AI and Machine Learning team at Strava. Have experience with exploratory data analysis and model prototyping, using languages such as Python or R and tools like Scikit learn, Pandas, Numpy, Pytorch, Tensorflow, Sagemaker.. Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, Snowflake).. Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, Go. We’re backed by Sequoia Capital, TCV, Madrone Partners and Jackson Square Ventures, and we’re expanding in order to exceed the needs of our growing community of global athletes.

Maxana is seeking a skilled Machine Learning Engineer to join our innovative team supporting a Fortune 500 client.. Strong programming skills in Python and experience with ML frameworks like PyTorch, TensorFlow, or scikit-learn. Experience with building and optimizing data pipelines using tools like Spark, Airflow, or similar. Experience with fraud detection, anomaly detection, or risk modeling in finance. Experience with cloud platforms (AWS, GCP, Azure) and containerization

Familiarity with data visualization tools (e.g., Matplotlib, Seaborn). Knowledge of big data technologies (e.g., Hadoop, Spark) is a plus. Experience with cloud platforms (e.g., AWS, Google Cloud, Azure). Master's or Ph. D. degree in Data Science, Statistics, Computer Science, or a related field.. Masters or Ph. D. degree in Data Science, Statistics, Computer Science, or a related field.

Machine Learning Engineer – AI-Powered Sustainability Tech. Our platform harnesses computer vision, data science, and predictive modeling to tackle real-world challenges in food waste management, operational efficiency, and resource optimization.. We’re looking for a Machine Learning Engineer to help design, build, and deploy production-grade ML models that optimize decision-making and automate key processes.. Work on computer vision, anomaly detection, and predictive modeling challenges. Strong experience with Python, TensorFlow, PyTorch, and modern ML frameworks

Model Development: Design, implement, and train machine learning models using state-of-the-art algorithms and frameworks including TensorFlow, PyTorch, scikit-learn. Strong proficiency in Python and ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch).. Knowledge of SQL/NoSQL databases and data pipeline tools (e.g., Apache Airflow).. Experience with cloud platforms (AWS, Azure, Google Cloud) and their Gen AI AI/ML services.. Get notified about new Machine Learning Engineer jobs in Sunrise, MN.

They will independently and collaboratively research, design, develop, and implement innovative AI, Machine Learning, deep learning, NLP, Cloud, and Data Science solutions to advance NYSE's analytics across various business lines.. 2+ years applying AI/ML/NLP/deep learning to financial market data. Strong knowledge of machine learning, AI, deep learning, NLP, and unstructured data analytics. Java, R, MATLAB, Scala, with frameworks like TensorFlow, Caffe, Spark, Hadoop. Knowledge of data management, analytics middleware, cloud computing (AWS), data visualization tools, GPU programming, and fog computing is a plus

We are currently looking for a talented and innovative Applied Science leader to bring our Machine Learning and Artificial Intelligence R&D capabilities to the next level.. Technical depth in machine learning systems (e.g. SageMaker, MLFlow), and deep learning frameworks (e.g. TensorFlow, PyTorch, MXNet etc). Strong publication record in top-tier ML and NLP conferences (e.g. ACL, NAACL, EMNLP, NeurIPS, ICML, AAAI, ICLR, SIGIR etc.. Experience with Big Data technologies such as AWS, Hadoop, Spark, Hive, Lucene/SOLR, or Kafka. Wellness Reimbursement for $300 per quarter for wellness activities including gym memberships, spa massages, workout equipment, meditation apps, and much more.

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.

Designs and implements Machine Learning (ML) and Deep Learning (DL) algorithms across multiple projects. Programs ML frameworks using Python and R. Develops and models software solutions utilizing Natural Language Processing (NLP), Information Retrieval, Machine Comprehension, Question Answering/Conversational AI, Reinforcement Learning, Knowledge Graphs, Causal Inference, and Design of Experiments. Conducts exploratory data analysis, unstructured data analysis, predictive analytics, and prescriptive analytics using Big Data, NLP, and chatbot technologies (Elasticsearch and Solr). Expertise in predictive modeling, training, and evaluating ML algorithms using Python and frameworks like scikit-learn, TensorFlow, PyTorch, or Keras, especially in Conversational AI and Search. Writing scalable, production-grade Python code, optimizing for performance, and low latency through techniques like quantization and knowledge distillation.