NCS is a leading information and communications technology (ICT) and communications engineering services provider across the Asia-Pacific region. We are headquartered in Singapore and a wholly owned subsidiary of the Singtel Group. We have in-depth domain knowledge and unique capabilities that create business value for customers. We offer a broad range of services, including consulting, systems development and integration, business process outsourcing, infrastructure management and solutions, and technology solutions.
What is the opportunity?
NCS is looking for a lead data scientist to address challenging data science problems by discovering information hidden in vast amount of data by leveraging techniques in statistics, machine learning, and data mining. The lead data scientist will work closely with the business users, project managers, and database engineers to develop sophisticated analytics algorithms that provide actionable insights. The role requires to a trusted adviser to our clients in conceptualizing advanced analytics solutions and enabling their journey in data science.
Being the lead, the role will pro-actively identify and develop advanced analytics models that help customers address business needs and challenges. The lead data scientist will also mentor and guide junior date scientists in fulfilling their tasks.
What will you do?
- Translate customer pain-points into problem statements, architect analytics solution, and engagingly present results and learnings to both technical and non-technical audiences.
- Develop and manage entire end-to-end lifecycle of scoping of data inputs, data cleaning and pre-processing, feature engineering, building models, deploying to production and improving models by iterations
- Present statistically sound model validations to justify model selection and performance
- Build and deploy highly valuable, efficient, scalable advanced analytics models in production systems
- Design and develop sophisticated visualizations and dashboards to explain the actionable insights
- Contribute to the data architecture engineering decisions to support analytics.
- Work closely with project manager and technical leads to provide regular status reporting and support them to refine issues/problem statements and propose/evaluate relevant analytics solutions
- Work in interdisciplinary teams that combine technical, business and data science competencies that deliver work in waterfall or agile software development lifecycle methodologies
- The range of accountability, responsibility and autonomy will depend on your experience and seniority, including:
- Contributing to our internal networks and special interest groups
- Mentoring to upskill peers and juniors
What do you need to succeed?
- Possess good communications skills to understand our customers' core business objectives and build end-to-end data centric solutions to address them
- Good critical thinking and problem-solving abilities
- Curiosity to ask why and tenacity to find the root causes
- Enthusiasm for implementing machine learning products through extensive experimentation from prototyping to production
- Stay up to date with evolving analytics concepts and data science platforms, tools, and techniques
- Ability to work independently and manage multiple task assignments
The ideal candidate should be/ possess:
- Post Graduate Degree (Masters or PhD) in Computer Science, Mathematics, Applied Statistics, Business Analytics or equivalent
- At least 6 years of experience in advanced analytics delivery
- Ability to communicate complex quantitative analysis in a concise and actionable manner
- Proficiency in manipulating and analysing complex, high-volume, high-dimensionality data (structures/unstructured) from varying sources
- Strong knowledge in Feature Selection/Extraction on a variety of data types
- Strong competency in various machine learning techniques (supervised/ unsupervised learning)
- Solid understanding of advanced analytics and statistics concepts (simulations, optimizations, clustering, regression, and neural networks, natural language processing, computer vision etc.)
- Expertise in Python/R, Apache Spark (or similar scripting language) coding capability to operationalize data analytics workflows & processes
- Experience in AIOps or Cyber Security Analytics
- Experience in MLOps
- Experience in data visualisation tools and libraries such as Tableau, Qlik, Shiny Plotly, ggplot2, etc
- Experience in machine learning model management and deployment tools using containerisation (Docker, Kubernetes)
Nice to have:
- Experience in Splunk MLTK or Elastic ML
- Experience in Amazon Web Services, Microsoft Azure, Cloudera, Hadoop, Spark, Storm or related paradigms and associated tools such as Pig, Hive, Mahout
- Experience with DevOps tools in analytics project delivery
- Experience with application/ software development and design
- Exposure in deep learning, and reinforcement learning job experience
- Experience in implementing Graph database analytics
- Knowledge in database modelling and data warehousing concepts