Data Science & ML
Explore live Data Science & ML jobs from active digital employers.
Sr Data Scientist, Amazon - Vertical Ads
Palo AltoSalary TBC <24 Hours
Applied AI ML - Payments Machine Learning (Associate)
LondonSalary TBC <48 Hours
EMEA Marketing Measurement Lead
LondonSalary TBC <48 Hours
Senior Software Engineer, Workspace Search Quality
BengaluruSalary TBC <48 Hours
Business Data Scientist, gTech Users and Products
DublinSalary TBC <48 Hours
Business Data Scientist, gTech Ads (English, Portuguese)
São PauloSalary TBC <48 Hours
Machine Learning Engineer, Sequence Models (project Sequoia)
New YorkSalary TBC <48 Hours
Machine Learning Engineer - Proactive
CupertinoSalary TBC <48 Hours
AIML - Sr Machine Learning Engineer, DMLI
CupertinoSalary TBC <48 Hours
Business Data Scientist, Subscriptions and Customer Growth Marketing
Mountain ViewSalary TBC <48 Hours
Senior Architecte IA/ML | Senior AI/ML Architect, Services professionnels pour le secteur public | Public Sector Professional Services
MontrealSalary TBC <48 Hours
Business Analyst Data (F/H)
69000Salary TBC <48 Hours
Senior Machine Learning Engineer
BeijingSalary TBC <48 Hours
Ingénieur(e) Data Scientist confirmé(e) - Industrie - Le Plessis Robinson
Le Plessis-RobinsonSalary TBC <48 Hours
Junior Data Scientist
SofiaSalary TBC <48 Hours
Data Scientist Decision Troopers / AI Advisory
Maassluis€5,000–€6,000 / month <48 Hours
Data Scientist
LimerickSalary TBC <48 Hours
Data Scientist
LimerickSalary TBC <48 Hours
Manager - Data Science & AI Consulting
BostonSalary TBC <72 Hours
Senior ML Data Scientist, Apple Pay Analytics
CupertinoSalary TBC <72 Hours
Senior Data Scientist - CTJ - TS/SCI
Reston$119,800–$261,000 <72 Hours
Audio Machine Learning Engineer, Google Beam
San FranciscoSalary TBC <72 Hours
Senior Applied AI/ML Engineer
BengaluruSalary TBC <72 Hours
Technical Support Engineer
Redmond$86,100–$187,400 <72 Hours
Machine Learning Engineer (Technical Leadership)
SingaporeSalary TBC <72 Hours
Explore Data Science & ML Jobs
ML engineers, AI researchers, predictive modeling
Although data science and machine learning are sometimes used interchangeably, they are different disciplines with distinct focuses. Data science is a wide-ranging and multidisciplinary field that uses data to solve organisational problems. It necessitates understanding business context, cleaning and structuring data, visualising data and carrying out exploratory analyses.
Machine learning is a subset of artificial intelligence (AI) and is a set of analytical tools that data scientists use to train their automated predictive models. Machine learning models train on previously-collected data to identify patterns which will allow them to more accurately predict future outcomes.
Predictive modelling uses historical data, machine learning and algorithms to detect patterns and trends within data, blending statistical techniques and a computational pipeline to help organisations make data-driven decisions. It’s a structured pipeline rather than one algorithm, where teams define their objective, gather and prepare data, determine which features will have the strongest influence on the target outcome, train the model on known data and then test and evaluate the model’s performance on unseen data to measure its accuracy.
Machine learning engineers are specialised software engineers, tasked with building, deploying and maintaining the AI systems that learn from data. They are the intermediary step between data science and production software engineering. They develop models that design, train and test machine learning and deep learning algorithms, and use frameworks such as TensorFlow and PyTorch. They deploy these models, integrating them into production environments and scalable software architectures. Once they’ve been deployed, engineers will monitor them to ensure accuracy and that the models are continuously retrained.
AI researchers come up with new ideas that may open up new possibilities in the use and application of artificial intelligence. Once they’ve had an idea, they will conduct research to establish what has previously been done and if there are gaps where this idea could fill in, before developing a research plan.
