Our Company

AITIS’s vision is to become a global leader in AI-Enabled Risk Assessment Technology enabling firms and individuals to save and improve lives and the environment. We focus on healthcare, construction safety, traffic management, and environmental monitoring.

Role and responsibilities

AITIS is expanding its Data Science team and looking for talented Data Scientists with hands-on experience of each stage of the Machine Learning pipeline, from data sourcing, through to model training and optimisation to data visualisation.

The candidates will be experienced in the acquiring and processing data as well apply a wide range of Machine Learning techniques to solve particular business problems with emphasis on advanced Deep Learning techniques.

We are looking for a ‘can do’ and pro-active person who can hit the ground running.

A background in Mathematics and/or Statistics is highly desirable.

Skills

Candidates should have direct hands-on professional experience in Computer Vision models for images and videos, create good models based on different types of data (including sensor data).

Supervised Learning	Experience in applying state of the art ML models to create robust, accurate solutions for analysing images and videos, as well as other data, to achieve: • Classification, segmentation and measurements • Object and people detection and identification • Traffic analysis • Visualisation Experience in applying state-of-the-art Deep Learning and other techniques to create robust and accurate financial credit score/risk assessment models: • Time series analysis • Probabilistic modelling • Statistics • Deep learning with sequential and high-dimensional data (RNNs, CNNs, Transformers)
Computer Vision/Image Processing and Signal Processing	Experience in applying state-of-the-art Deep Learning techniques on images and videos and other data for: • CV models, e.g. Yolo, ResNet, VGG • Deep Learning architectures and models, e.g. CNN, RNN, Transformers • Modern image pre-processing techniques, e.g. CutMix, MixUp • Postprocessing, e.g. DeepSort • Audio analysis, e.g. source separation, event detection • Other signal processing, e.g. transforms, filtering
Unsupervised Learning Techniques	Including unsupervised learning tasks such as Clustering, Anomaly Detection, Dimensionality Reduction (e.g. Auto-Encoders, Embeddings) to augment the Financial credit score/risk assessment models.
Model Explainability	Experience in applying a range of algorithms and techniques to create interpretable descriptions for the Computer Vision and other models.
Visualisation	Apply visualisation techniques to display results of models for images, videos and other modalities.