1 July 2021
Our client is looking for an experienced lead data scientist to lead a team to design, develop and implement data science models and algorithms across multiple data science projects.
Someone with track record in deploying Data Science / Artificial Intelligence models in production environment will be preferred.
- PhD / Bachelors in Computer Science, Computer Engineering, Statistics, Applied Mathematics, Data Science and Analytics;
- Has around 5 years of experience in Data Engineering or Data Science field;
- Proven to be able to lead and solve complicated technical problems in Data Engineering or Data Science areas;
- Good hands on experience solving business problems using Natural Language Processing ( NLP) , Machine Learning ( ML ) , Statistical & Predictive Modelling;
- Experienced in working with large data sets (statistical analysis, data visualization, data mining, data cleansing, data transformation and machine learning);
- Good hands-on in visualization tools such as Qlikview / Qliksense, Tableau &/or Power BI and Python skills;
- Experience in using and adapting algorithms from machine learning libraries such as scikit-learn, numpy / scipy, pandas, NLTK &/or Tensorflow;
- Able to apply scientific thinking to solving business problems through analytics and data;
- Possess excellent written, verbal and presentation skills.
- Experience in big data tools (Hive, Hadoop, Spark, Elastic Stack, Kafka) and databases (Cassandra, MongoDB, and Neo4J).
- Collaborate with business owners to translate and formulate business requirements into technical specifications through data science approach;
- Come up with ideas, experimenting, forming hypotheses and testing them out;
- Translate business requirements into a problem that can be quantified and solved with analytics and data and regular review of these productionized models;
- Design, develop and implement strategies and workflow to roll out a robust, scalable, and efficient data refinery related solutions using suitable data engineering;
- Identify trends, develop insights, build analytic models and present key findings to the senior management;
- Interpret technical statistical results and communicate insights through storytelling;
- Maintain and enhance data repository to ensure continuing relevance and consistency to business users;
- Ensures documentation of processes, analytics design, measure definitions, data integration, and development;
- Mentor and champion Data Analytics driven activities within the team;
- Keep abreast of latest developments in the fields of Data Science / Artificial Intelligence technologies and capacities.