Data Scientist

Kasi Jeyaseelan Naveen (Proprietor of Arient Solutions); Singapore; On-site; SGD 90,000 - 180,000; Full time

Job summary

Arient Solutions, a Singapore-based data analytics consultancy, seeks a Data Scientist to design, develop, and deploy advanced analytics and ML solutions. You will work with stakeholders, project managers, and engineers to translate business challenges into production-ready data science solutions.

The role blends hands-on analytics, applied research, and client advisory responsibilities to support organisations on their data science and AI journey.

Qualifications

• Strong ability to communicate complex quantitative analysis concisely.
• Experience with high-volume, high-dimensional data.
• Expertise in feature selection and feature engineering.
• Solid grounding in supervised and unsupervised ML.
• Deep understanding of analytics (stats, NLP, optimisation, simulation).
• Strong programming in Python and/or R; familiarity with Spark.
• Experience using LLMs for GenAI/Agentic AI.
• Hands-on data visualization with Tableau/Qlik/Plotly/Shiny.
• Experience deploying models with Docker and Kubernetes.

Responsibilities

• Translate client pain points into analytical problems and architectures.
• Build end-to-end data science workflows from data to deployment.
• Apply ML, NLP, optimization, and simulation to business problems.
• Validate models and justify choices with metrics.
• Develop scalable models for production and manage deployments with Docker/Kubernetes.
• Create dashboards and visuals to communicate insights to stakeholders.
• Present results to technical and non-technical audiences.
• Collaborate with project managers and engineers; refine analytics requirements.
• Mentor junior data scientists and contribute to internal communities.

Skills

Python

R

Feature engineering

Machine learning

Statistics

Data visualization

LLMs / GenAI

Model deployment

Big data analytics

Communication of insights

Education

Master's or PhD in Mathematics/Statistics/Analytics

Tools

Tableau

Qlik

Plotly

ggplot2

Shiny

Docker

Kubernetes

Apache Spark

AWS

Job description

Job de ion

As a Data Scientist, you will design, develop, and deploy advanced analytics and machine learning solutions that uncover hidden insights from large, complex datasets. You will work closely with business stakeholders, project managers, and engineering teams to translate real-world business challenges into production-ready data science solutions. This role combines hands‑on analytics development, applied research, and client advisory responsibilities, supporting organisations on their data science and AI journey.

What will you do

Applied Data Science & Advanced Analytics

• Translate customer pain points into clear analytical problem statements and solution architectures.
• Design, build, and iterate end‑to‑end data science workflows, from data ingestion and preprocessing to feature engineering, modelling, and deployment.
• Apply statistical analysis, machine learning, NLP, optimisation, and simulation techniques to solve complex business problems.
• Perform statistically sound model validation and clearly justify model selection and performance.

Model Engineering & Production Deployment

• Build scalable, efficient machine learning models for deployment in production systems.
• Operationalise analytics workflows using Python/R and distributed processing frameworks such as Apache Spark.
• Deploy and manage models using containerisation and orchestration tools (e.g., Docker, Kubernetes).
• Leverage LLMs to build GenAI or Agentic AI solutions where appropriate.

Insights Communication & Visualisation

• Design and develop impactful dashboards and visualisations to communicate actionable insights.
• Present results, learnings, and recommendations clearly to both technical and non-technical audiences.
• Act as a trusted adviser to clients in conceptualising and evaluating advanced analytics solutions.

Collaboration & Delivery

• Work closely with project managers and technical leads to provide regular status updates and refine analytics requirements.
• Contribute to data architecture and engineering decisions that support analytics use cases.
• Participate in interdisciplinary teams delivering projects using Agile or Waterfall methodologies.

Knowledge Sharing & Mentorship

• Contribute to internal communities of practice and special interest groups.
• Mentor and upskill junior data scientists and peers, depending on seniority.

Qualifications

The ideal candidate should possess:

Must-have

• Strong ability to communicate complex quantitative analysis in a concise, actionable manner.
• Proven experience working with high-volume, high-dimensional structured and unstructured data.
• Strong expertise in feature selection and feature engineering across diverse data types.
• Solid grounding in machine learning techniques (supervised and unsupervised).
• Deep understanding of advanced analytics (statistics, NLP, optimisation, simulation).
• Strong programming skills in Python and/or R; experience with Apache Spark or similar frameworks.
• Experience using LLMs for GenAI or Agentic AI solution development.
• Hands‑on experience with data visualisation tools and libraries (e.g., Tableau, Qlik, Plotly, ggplot2, Shiny).
• Experience in model deployment and lifecycle management using Docker and Kubernetes.

Nice to have

• Postgraduate degree (Master's or PhD) in Mathematics, Statistics, Business Analytics, or a related field.
• Prior consulting experience in AI and data analytics domains.
• Experience delivering advanced analytics solutions or conducting applied research.
• Exposure to cloud and big data platforms (AWS, Azure, Hadoop, Spark, Cloudera).
• Experience with DevOps practices in analytics delivery.
• Background in application or software development.
• Exposure to deep learning, reinforcement learning, or graph analytics.
• Knowledge of database modelling and data warehousing concepts.