Data Scientist
ASTEK has been providing IT and Engineering solutions for some of the world’s largest industrial and services groups for more than 35 years with 7200 passionate experts in 22 countries throughout Europe, APAC, Middle East and the Americas
Currently, we are looking for Data Scientist which would be based in Singapore.
Responsibilities:
- You will apply machine learning and possibly deep learning techniques to a variety of modelling and relevance problems
- You will participate in the engineering life-cycle including daily scrum, sprint reviews, sprint retrospectives, community of practices etc.
- You will have to review, regularly, for performance improvements and decide which AI technologies and algorithms can be used in a production environment
- You will partake in data exploration activities for businesses, uncovering patterns in the data usage
- Extracting actionable insights from diverse data sources through data mining techniques
- You will design and apply algorithms to identify key features, build, and fine-tune models
- You will evaluate and identify relevant datasets and create data dictionaries when applicable
- You will be handling data processing, cleansing, and validation to ensure its suitability for analysis
Requirements:
- Bachelor’s degree with minimum 5 years of data science experience
- You should have a good repertoire of software tools and programming languages to which you can apply to building a PoC
- You should have good working knowledge about Machine Learning and/or Deep Learning algorithms in the realm of supervised, unsupervised, reinforcement not limited to ANN, CNN, RNN, GAN
- You should have good working knowledge in statistical classification domain eg. Logistic Regression, K-nn, Kernel SVM, Naïve Bayes, Decision Tree, Random Forest
- You should have good working knowledge in clustering domain e.g. K-means clustering, hierarchical clustering
- You should have a good understanding of applying gradient boosting in regression and classification problems. E.g. XGBoost
- You should have good working knowledge in anomaly detection and outlier detection techniques e.g. DBSCAN, Gaussian Mixture Models
- You should have deep understanding and experience with large language modeling techniques, such as GPT, BERT, or Transformer-based architectures
- You should be familiar with large language modeling frameworks like OpenAI's GPT, Hugging Face's Transformers, or Google's BERT
- You should have knowledge of natural language processing (NLP) concepts and techniques, such as tokenization, word embeddings, and sequence modelling
- You should have proficiency in reinforcement learning algorithms and concepts. This includes understanding the basics of Markov Decision Processes, Q-learning, policy gradients, and value iteration
- You would have working knowledge of one or more programming languages like C & Python and you should be able to explain the underlying mechanics in addition to the theoretical Machine/Deep Learning model
