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 ❯
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 ❯
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 ❯
