Snr Data Scientist, Data & AI Engr

Singtel Group; Singapore; On-site; SGD 100,000 - 125,000; Full time

Job summary

A leading technology services firm is seeking a Data Scientist to drive data-driven projects, manage a team, and collaborate with cross-functional teams. Candidates should have strong analytical skills, communication ability, and at least 2 years of experience in advanced analytics. The role includes developing models, presenting results, and ensuring the scalability of data solutions.

Qualifications

• At least 2 years of experience in advanced analytics delivery.
• Proficiency in manipulating high-volume, complex data.
• Strong skills in feature selection across data types.

Responsibilities

• Develop and manage end-to-end data lifecycle processes.
• Present model validations and justify selections to stakeholders.
• Collaborate with interdisciplinary teams to deliver analytics solutions.

Skills

Problem Solving

Critical Thinking

Communication

Machine Learning

Curiosity

Data Manipulation

Education

Bachelor's degree in a relevant field

Tools

AWS

Azure

Docker

Tableau

Job description

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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 a Data 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 data inputs, data cleaning and pre-processing, feature engineering, building models, deploying to production, and improving models through 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 data architecture engineering decisions to support analytics.
• Work closely with project managers and technical leads to provide regular status reports and support issue/problem statement refinement, proposing and evaluating relevant analytics solutions.
• Work in interdisciplinary teams combining technical, business, and data science competencies, delivering work in waterfall or agile methodologies.
• The range of accountability, responsibility, and autonomy will depend on your experience and seniority, including:
• Contributing to internal networks and special interest groups.
• Mentoring peers and juniors to upskill them.

The ideal candidate should possess:

• Good communication skills to understand core business objectives and build end-to-end data-centric solutions.
• Strong critical thinking and problem-solving abilities.
• Curiosity and tenacity to find root causes.
• Enthusiasm for implementing machine learning products through extensive experimentation from prototyping to production.
• Up-to-date knowledge of evolving analytics concepts, tools, and techniques.
• Ability to work independently and manage multiple tasks.
• At least 2 years of experience in advanced analytics delivery or research for full-time applicants, or relevant academic exposure for interns.
• Ability to communicate complex analyses concisely and actionably.
• Proficiency in manipulating and analyzing complex, high-volume, high-dimensional data from various sources.
• Strong skills in feature selection/extraction across data types.
• Solid understanding of machine learning techniques (supervised/unsupervised).
• Knowledge of advanced analytics areas such as Statistics, NLP, Simulation, Optimization, etc.
• Experience with visualization tools and libraries like Tableau, Qlik, Shiny, Plotly, ggplot2, etc.
• Experience with model management and deployment tools using containerization (Docker, Kubernetes).
• Experience with cloud platforms such as AWS, Azure, and big data tools like Hadoop, Spark, Storm.
• Experience with DevOps practices in analytics projects.
• Experience in software development and design.
• Exposure to deep learning and reinforcement learning.
• Experience with graph database analytics.
• Knowledge of database modeling and data warehousing concepts.