Snr Data Scientist, Data & AI Engr

NCS is a leading technology services firm that operates across the Asia Pacific region in over 20 cities, providing consulting, digital services, technology solutions, and more. We believe in harnessing the power of technology to achieve extraordinary things, creating lasting value and impact for our communities, partners, and people. Our diverse workforce of 13,000 has delivered large-scale, mission-critical, and multi-platform projects for governments and enterprises in Singapore and the APAC region.

As aData Scientist, you will be responsible for driving data-driven initiatives, managing a team of data scientists, and collaborating closely with cross-functional teams to deliver innovative data solutions for clients.

What you will do:

• Translate customer pain-points into problem statements, architect analytics solutions, and engagingly present results and learnings to both technical and non-technical audiences.
• Develop and manage the entire end-to-end lifecycle of scoping 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 actionable insights.
• Contribute to the data architecture engineering decisions to support analytics.
• Work closely with project managers 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, delivering work in waterfall or agile software development lifecycle methodologies.
• The range of accountability, responsibility, and autonomy will depend on your experience and seniority, including:
  1. Contributing to our internal networks and special interest groups.
  2. Mentoring to upskill peers and juniors.

The ideal candidate should possess:

• Good communication 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.
• 2 years of experience in advanced analytics delivery/research for full-time applicants or academic exposure for interns applying for this role.
• Ability to communicate complex quantitative analysis in a concise and actionable manner.
• Proficiency in manipulating and analyzing complex, high-volume, high-dimensionality data (structured/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 (Statistics, NLP, Simulation, Optimizations, etc.).
• Expertise in Python/R, Apache Spark (or similar scripting language) coding capability to operationalize data analytics workflows & processes.
• Experience in data visualization tools and libraries such as Tableau, Qlik, Shiny, Plotly, ggplot2, etc.
• Experience in machine learning model management and deployment tools using containerization (Docker, Kubernetes).
• 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 modeling and data warehousing concepts.