Data Science & Machine Learning Jobs

Strong proficiency in Python, R (must-have) with experience in libraries such as Scikit-learn, and TensorFlow/PyTorch. Expertise in supervised and unsupervised machine learning techniques, including regression, classification, clustering, anomaly detection, and deep learning. Hands-on experience with Dataiku for end-to-end data science and data visualization tools (e.g., Looker, Power BI). Experience within Hi-Tech / Telecommunication specific industry/domain such as Supply Chain, Accounting, Sales etc. Non-Sales employees may be eligible for a discretionary incentive bonus, while Sales employees may be eligible for a sales commission.

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

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.

We are seeking a Principal Applied Machine Learning Engineer to be the foundational hire responsible for establishing the company's machine learning capabilities.. Develop models for classification, regression, NLP, and LLM-based use cases, aligned to critical business needs in operations, payments, and product workflows.. Deep hands-on experience with AWS ML services, especially SageMaker and Bedrock.. Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.. Familiarity with big data tools like Spark, Kafka, or Hadoop.

About the job Data Science Analyst r. Create and use advanced machine learning algorithms and statistics: regression, distributions, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.. Work to analyze data from a wide variety of 3rd party data providers: Google Analytics, Adwords, Adobe, etc.. Use distributed data/computing tools to achieve business results: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.. g S3, Spark, DigitalOcean, Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL

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.

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.

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 Experimental Design. Conducts exploratory data analysis, predictive analytics, and prescriptive analytics leveraging Big Data, NLP, and chatbot technologies such as Elasticsearch and Solr. Promotes the use of ML and DL frameworks like TensorFlow, Keras, MXNET, and H2O. Expertise in predictive modeling, training, and evaluating ML algorithms using Python and frameworks like scikit-learn, TensorFlow, PyTorch, Keras, especially in Conversational AI and Search applications. Experience designing NLP and NLU solutions such as Chatbots, Information Retrieval, NER, Summarization, and Text classification using NLP, ML, DL, and embedding techniques like LSTM and BERT.

Its patent-pending approach uniquely combines advances in data science and technology (AI, machine learning, cloud computing) to transform risk management.. Deep understanding of machine learning/statistical algorithms such as time series analysis and outlier detection, neural networks/deep learning, boosting and reinforcement learning.. Expertise in an analytical language (Python, R, or the equivalent), and experience with databases (GCP, SQL, or the equivalent). Experience in building NLP solutions and/or GEN AI are strongly preferred. 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy

Role: Data Scientist - AI Strategy & Implementation(This role is open to US Citizens, Green Card holders, GC-EAD only.. As a Data Scientist / Data Analyst / Data Science Specialist for Adidev Technologies Inc., you will be enhancing and debugging large-scale applications for one of our well-known clients.. Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation-systems, environmental systems and/or agronomic problems.. Practical knowledge and experience with cloud-computing systems and platforms, including the routine deployment of pipelines through Kubernetes. Knowledge of Python libraries (NumPy, Pandas, SciKit-Learn, TensorFlow, PyTorch), Spacy, MongoDB, PostgreSQL, Flask, streamlet and a good knowledge of data pipelines constructionMust have

About the job Data Science Analyst r. Create and use advanced machine learning algorithms and statistics: regression, distributions, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.. Work to analyze data from a wide variety of 3rd party data providers: Google Analytics, Adwords, Adobe, etc.. Use distributed data/computing tools to achieve business results: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.. g S3, Spark, DigitalOcean, Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL

Profound experience and theoretical understanding of advanced quantitative methods in statistical, machine learning and deep learning. Extensive experience with SQL, Python, and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).. Significant experience with cloud platforms (e.g., AWS, Microsoft Azure, Databricks, Kubernetes).. Experience in advanced modeling techniques, including numerical optimization algorithms, natural language processing (NLP), large language models (LLM) and reinforcement learning.. Access to additional benefits like mental healthcare as well as fertility and adoption assistance.

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)

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.

The Data and Artificial Intelligence Platform (DAP) group is a key component of Visa’s Technology organization that provides the enabling technology and processes to handle Visa’s data assets and deliver valuable information, products, and services to customers.. Experience with data science tools and technologies (e.g., TensorFlow, PyTorch, scikit-learn). Expert knowledge in Deep Learning techniques and LLM (Large Language Model).. Experience working with Airflow, GitHub, ML flow for building and maintaining ETL pipeline.. Proficient in advanced data mining and statistical modeling techniques, including Predictive modeling (e.g., binomial, and multinomial regression, ANOVA), Classification techniques (e.g., Clustering, Principal Component Analysis, factor analysis), Decision Tree techniques (e.g., CART, CHAID).

Our team has a mix of highly proficient people from multiple fields such as Machine Learning, Data Science, Software Engineering, Operations, and Big Data Analytics.. As Director, you will set the vision and lead the execution of a portfolio of high-impact research initiatives that blend applied science, scalable engineering, and domain expertise in commerce.. Identify emerging research areas aligned with eBay’s business priorities, such as AI-driven taxonomy evolution, multimodal understanding (image + text), and dynamic knowledge graph enrichment.. Oversee the design, development, and deployment of ML/AI systems related to entity resolution, attribute extraction, taxonomy generation, knowledge graph construction, and product normalization.. Industrial experience with multiple of the following: ML architecture, deployment optimization, classification, regression, NLP, clustering, Deep Learning / Neural Networks, Reinforcement Learning, or related

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 Experimental Design.. Conducts exploratory data analysis, predictive analytics, and prescriptive analytics leveraging Big Data, NLP, and chatbot technologies such as Elasticsearch and Solr. Promotes the use of ML and DL frameworks like TensorFlow, Keras, MXNET, and H2O.. Expertise in predictive modeling, training, and evaluating ML algorithms using Python and frameworks like scikit-learn, TensorFlow, PyTorch, Keras, especially in Conversational AI and Search applications.. Experience designing NLP and NLU solutions such as Chatbots, Information Retrieval, NER, Summarization, and Text classification using NLP, ML, DL, and embedding techniques like LSTM and BERT.

Strong development experience with any of the following software languages: Python, R, SQL, or Scala.. Professional knowledge of Machine Learning Algorithms: NLP, Neural networks, deep learning, Naïve Bayes, regression, random forest, clustering, and text mining.. Strong experience with common ML libraries and frameworks (ex: PyTorch, Tensorflow, Scikit-Learn, MLflow, Sagemaker, Kubeflow, Spark MLlib, HuggingFace, or LangChain).. Proficiency with data platforms such as Databricks, Snowflake, BigQuery, Spark in any flavor, HIVE, Hadoop, Cloudera, or RedShift (we use Databricks).. Our People First Culture celebrates diversity, equity and inclusion not simply because it’s the right thing to do, but also because it’s the key to our success.

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.