Data Science & Machine Learning Jobs

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

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.

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)

Identify and Ideate on Data & Analytics and Artificial Intelligence initiatives to meet Customer needs and /or achieve & exceed Segment goals. 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)

State of the art prediction models for estimating food preparation times, batching quality as well as time spent by couriers at restaurants picking up items.. You will be in-charge of solving Uber scale problems with the right techniques like reinforcement learning/deep learning/optimization methods.. Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib. Experience with SQL and database systems such as Hive, Kafka, Cassandra, etc. Experience in modern deep learning architectures and probabilistic models.

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.

Apply statistical and machine learning techniques (clustering, regression, NLP, deep learning, etc. Develop and deploy models using Python or R, leveraging tools like scikit-learn, TensorFlow, and PyTorch.. Experience: 1–3 years in a data science role with hands-on experience in analytics and modeling.. Proficiency in Python (pandas, numpy, scikit-learn) and data visualization tools (Power BI, Tableau, etc. Familiarity with cloud platforms (Azure, AWS, or GCP) and SQL-based data environments.

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. Our Senior Researchers tackle such diverse and challenging projects on Image Quality scoring; Automatic Taxonomy Improvement; Entity Resolution of rich, hierarchical Entities; and Conflict Resolution between different representations of the same Entity. Data Strategy: Partner with the operations and data teams to ensure access to high-quality labeled data, and proactively shape data acquisition strategies where needed. Deep understanding of modern ML approaches including classification, regression, NLP, clustering, deep learning, and/or reinforcement learning. Proficiency with big data processing frameworks such as Hadoop, Spark, and SQL.

As a Applied Artificial Intelligence- Machine Learning- Senior Associate, on the Instrumentation & Metrics (I&M) team, you are in integral part of the team that will be responsible for leveraging your expertise in data science; machine learning to develop and maintain production grade models using various analytical techniques.. Additionally, you will guide the team on best practices and techniques in data science; machine learning, ensuring the effective use of data to drive business decisions.. The Data Science & Analytic Solutions (DSAS) team is responsible for analytic support of the Commercial Investment Bank (CIB) Payments organization.. Job responsibilitiesDesigns, develops, and deploy machine learning models in production environmentsUtilizes Python and machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn,. Proven track record of building and implementing machine learning models in production environmentsProficiency in Python, with extensive experience using machine learning frameworks and libraries such as TensorFlow, PyTorch, and Scikit-learn.

Preferred/Desired skills Big Data, Hadoop, HIVE/HQL, HDFS, Spark/PySpark, Kafka. Proven experience in developing models using Regression, Decision trees, Bayesian networks, Random Forest, Logistic regression, Support vector machine, Gradient boosting algorithms, Clustering algorithms and Dimensionality Reduction Algorithms.. Experience in Deep Learning model development using Tensorflow, Keras, PyTorch. Working experience in Python, R and SQL. Experience in Python data science packages such as numPy, SciPy and SciKit-learn

Essential Job Functions AI Model Development – Design, build, and train machine learning and deep learning models, including GenAI, NLP, and predictive analytics solutions for healthcare applications.. Extensive AI/ML Experience: 5+ years in AI/ML engineering, including hands-on work with GenAI, NLP, deep learning, and computer vision.. Technical Proficiency: Strong coding skills in Python and frameworks like TensorFlow and PyTorch; experienced with LLMs (e.g., GPT, Gemini, Claude) including prompt engineering and optimization.. Scalable Deployment Skills: Familiar with cloud platforms (AWS, Azure, GCP), MLOps practices, and big data tools (Spark, Hadoop, SQL/NoSQL).. Fully Remote Opportunity – Work from anywhere in the U.S.

Data Governance & Ethics: •Ensure data integrity and privacy by following best practices in data handling and processing.. Technical Skills: •Strong proficiency in programming languages such as Python, R, or Java. •Solid knowledge of statistical analysis and machine learning techniques.. Familiarity with cloud platforms such as AWS, GCP, or Azure is a plus.. Experience with deep learning frameworks like TensorFlow, Keras, or PyTorch.. Knowledge of NLP (Natural Language Processing) and computer vision techniques.

Design, build, and train machine learning models using appropriate algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning).. Use various machine learning and AI frameworks (TensorFlow, PyTorch, Scikit-learn, Keras, etc.). Develop and optimize algorithms for specific use cases like image recognition, natural language processing (NLP), speech recognition, or recommendation systems.. Strong programming skills in Python, R, or similar languages for machine learning and data analysis.. Deep knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost.

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.

Data Science Projects : Design, develop, and implement predictive and explanatory analyses that address critical business problems, leveraging expertise in machine learning, Natural Language Processing (NLP), and other areas. Develop Modeling Capabilities : Hands-on experience with frameworks like TensorFlow, PyTorch, Transformers, etc., focusing on scalability, performance, and interpretability. Strong Analytical Skills : Apply analytical thinking to diagnose business needs and to establish analytical hypothesis and solutions; Have outstanding knowledge of Hive, SQL, and Python encompassing data manipulation and statistical modeling/data-mining techniques; Leverage predictive modeling to identify tactics for channel optimization of existing areas and conceptualize opportunities. Familiarity with machine learning libraries and frameworks (e.g., Tensorflow, PyTorch, NLTK, scikit-learn) and experience applying algorithms to real-world business problems. 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy

Design, build, and train machine learning models using appropriate algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning).. Use various machine learning and AI frameworks (TensorFlow, PyTorch, Scikit-learn, Keras, etc.). Develop and optimize algorithms for specific use cases like image recognition, natural language processing (NLP), speech recognition, or recommendation systems.. Strong programming skills in Python, R, or similar languages for machine learning and data analysis.. Deep knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost.

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

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

As a Staff Data Scientist at Cint, you will be responsible for designing and developing advanced statistical and machine learning methodologies across our Media Measurement and Data Solutions product lines. e.g., properties of distributions, hypothesis testing, multivariate (parametric/ non-parametric) testing, sampling theory, weighting/projection, experimental design, regression/predictive modeling, causal inference techniques, stochastic modeling / simulation, and more. Proficiency with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and techniques (e.g., clustering, regression, tree-based models).. Advanced SQL skills and familiarity with big data technologies (Spark, Hadoop, Databricks). In June 2021, Cint acquired Berlin-based GapFish – the world’s largest ISO certified online panel community in the DACH region – and in January 2022, completed the acquisition of US-based Lucid – a programmatic research technology platform that provides access to first-party survey data in over 110 countries.

Provide advanced actuarial and analytical skills to support the development or enhancements of commercial data-solutions at RGA. Have a strong understanding on tools / techniques their actuarial peers will not have had a formal education in: Applications, risks, transparency, quality assurance & peer review, ethical guidelines Staying abreast of new techniques, but focusing on practical applications Liaising with RGA's Global D&A team for more sophisticated data science applications Contributing to RGA's global analytics community, routinely sharing, maintaining consistency of approach.. Spreadsheet skills (Excel/VBA) and database applications (SQL, Snowflake, Oracle,.. Advanced predictive modeling skills: Tree-based models + GLMs Cross-Validation, Residuals and model diagnostics Basic Statistical concepts for feature engineering (e.g. percentiles, standardization, correlations, risk ratios / chi-square test, splines, and other non-linear transformations) Pro-active use of insurance expertise & actuarial concepts to feature engineering and model evaluation.. Basic machine learning models/concepts (SVM's, GAN's, Neural Networks/Deep Learning, Naive Bayes, NLP), and/or basic statistical concepts for feature engineering for dimensionality reduction such as PCA's, SVD's, and clustering