understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization. • Use Deep Learning frameworks like MXNet, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our internal customers build DL models. • Use SparkML and Amazon Machine Learning (AML) to help our internal customers build ML models. • Work … the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization. - Skilled in using Deep Learning frameworks (MXNet, Caffe2, TensorFlow, Theano, CNTK, Keras) and ML tools (SparkML, Amazon Machine Learning) to build models for internal customers. PREFERRED QUALIFICATIONS - 7+ years of IT platform implementation in a technical and analytical More ❯
understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization. • Use Deep Learning frameworks like MXNet, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our internal customers build DL models. • Use SparkML and Amazon Machine Learning (AML) to help our internal customers build ML models. • Work … the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization. - Skilled in using Deep Learning frameworks (MXNet, Caffe2, TensorFlow, Theano, CNTK, Keras) and ML tools (SparkML, Amazon Machine Learning) to build models for internal customers. PREFERRED QUALIFICATIONS - 10+ years of IT platform implementation in a technical and analytical More ❯
Platforms, Frameworks, and EC2 Instances. Deliver ML/DL projects end-to-end: understand needs, data aggregation, exploration, model building & validation, deployment, and impact delivery. Utilize frameworks like MXNet, Caffe2, TensorFlow, Theano, CNTK, Keras for DL models. Use SparkML and Amazon Machine Learning for ML models. Collaborate with Big Data and DevOps teams to analyze, normalize, label data, and operationalize … with internal or external customers. Experience delivering end-to-end ML/DL projects, understanding business needs, data handling, model building, validation, and deployment. Skilled in DL frameworks (MXNet, Caffe2, TensorFlow, Theano, CNTK, Keras) and ML tools (SparkML, AML). 7+ years in IT platform implementation, consulting, and distributed solutions design. Experience with databases (SQL, NoSQL, Hadoop, Spark, Kafka, Kinesis More ❯
science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model More ❯
in Engineering, Computer Science or Electronics; or equivalent experience. 5+ years of relevant experience. Solid practical experience of: Languages: Python, C/C++, OpenCL, CUDA ML Frameworks: TensorFlow, PyTorch, Caffe2, ONNX, OpenVx Linux Environment Task and Data parallel/concurrent systems AI Network optimisations: quantization, compression (pruning etc.) Experience developing and delivering on projects using best industry practices and methods. More ❯
science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model More ❯
science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model More ❯
science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model More ❯
science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model More ❯
science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model More ❯
in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model More ❯
science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model More ❯
concepts including developing models and tuning the hyper-parameters, as well as deploying models and building ML service • Experience with computer vision algorithms and libraries such as OpenCV, TensorFlow, Caffe or PyTorch. • Technical expertise, experience in Data science and ML Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need More ❯
Lead Data Scientist London Up to £95,000 + bonus + benefits Our Client We're helping a global ecommerce marketplace build out their Data & Analytics teams. With over 20,000 employees in 41 locations around the world, our client More ❯
