Data Scientist

ELLIOTT MOSS CONSULTING PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Elliott Moss Consulting PTE. LTD. is seeking an experienced Data Scientist to join the Data Science and AI team. You will tackle strategic housing-related problems by building data-driven solutions and deploying them for scalable impact.

You will collaborate with IT, data engineering, and business units to translate challenges into analytical requirements, employing modern AI and ML techniques throughout the project lifecycle.

Qualifications

• Bachelor's or master's degree in a quantitative field.
• Strong problem-solving with MECE and root cause approaches.
• Hands-on MLOps—development, deployment, monitoring, lifecycle management.
• Experience deploying ML/AI solutions in cloud environments, especially AWS.
• Knowledge of transformers, RL, statistical modelling, optimization, and multi-criteria decision analysis.
• Experience with AI engineering: LLMs, Generative AI, embeddings, vector databases, LangChain/LangGraph.

Responsibilities

• Apply advanced analytics and ML techniques to strategic business problems.
• Develop and deploy end-to-end ML/AI solutions using MLOps and AWS.
• Ensure data science solutions are scalable, accurate, and aligned with business needs.
• Collaborate with IT, data engineering, and business units; translate business challenges into technical requirements.
• Present findings and recommendations clearly to stakeholders.
• Contribute to data engineering activities and maintain best practices.

Skills

Python

R

SQL

Shell scripting

MECE problem solving

MLOps

AWS

Transformers

Generative AI

Education

Bachelor's or master's degree in computer science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field

Tools

LangChain

LangGraph

Vector databases

Cloud platforms (AWS)

Job description

Job Description

As a Data Scientist, you will be a core member of the Data Science and AI team, working on challenging and strategic business problems to develop effective, data-driven solutions that enable the delivery of quality and affordable housing.

You will work closely with technical and business stakeholders to identify opportunities, solve complex problems, develop and deploy advanced analytical and AI solutions, and ensure that the solutions delivered are accurate, scalable, efficient, and aligned with business objectives.

Key Responsibilities

• Contribute as a core member of strategic and high-impact Data Science and AI projects, applying advanced analytical and machine learning techniques to solve complex business problems.
• Participate in problem-solving exercises, including root cause analysis, structured problem-solving, MECE frameworks, and other appropriate methodologies.
• Support the selection, development, validation, implementation, and deployment of machine learning and AI algorithms.
• Take ownership of the correctness, efficiency, scalability, and integrity of data science solutions developed by the team.
• Contribute to the team's technical workflows, processes, methodologies, and best practices.
• Review and provide constructive feedback on peers' work to maintain high technical standards and ensure adherence to best practices.
• Collaborate closely with IT, data engineering, system project teams, and business units to understand requirements and co-create high-impact, business-focused solutions.
• Translate business challenges into analytical and technical requirements and communicate data-driven insights effectively to stakeholders.
• Develop and deploy end-to-end machine learning and AI solutions using MLOps practices and cloud platforms such as AWS.
• Apply advanced techniques in deep learning, statistical modelling, optimization, multi-criteria decision analysis, and AI engineering.
• Work with modern AI technologies, including Large Language Models (LLMs), neural embeddings, Generative AI, vector databases, LangChain, LangGraph, and related AI frameworks.
• Contribute to data engineering activities supporting data science, including ELT processes, structured data transformation, database technologies, and data preparation.
• Stay current with emerging developments in Data Science, AI, Machine Learning, and Generative AI, and continuously enhance technical capabilities and team practices.
• Present findings, models, visualizations, and recommendations clearly to both technical and non-technical stakeholders.

Qualifications & Requirements

• 5+ years of professional experience in Data Science, Machine Learning, AI, or a closely related field with a reputable organization.
• Bachelor's or master's degree in computer science, Data Science, Statistics, Mathematics, Engineering, or another relevant quantitative discipline.
• Strong problem-solving capabilities with proficiency in structured approaches such as MECE, root cause analysis, and hypothesis-driven problem solving.
• Strong understanding of MLOps principles and hands‑on experience in end‑to‑end model development, deployment, monitoring, and lifecycle management.
• Strong experience deploying machine learning and AI solutions in cloud environments, particularly AWS.
• Strong theoretical and practical knowledge of machine learning and deep learning algorithms and architectures, including areas such as:
  • Transformer architectures
  • Reinforcement Learning
  • Statistical modelling
  • Optimization techniques
  • Multi-criteria decision analysis
• Hands‑on experience in AI Engineering, including:
  • Large Language Models (LLMs)
  • Generative AI
  • Neural embeddings
  • Vector databases
  • LangChain and/or LangGraph
  • Development of AI/GenAI applications and systems
• Experience with data engineering for Data Science, including ELT concepts, structured data transformation, database technologies, and data preparation.
• Strong programming and development skills in:
  • Python and relevant data science/ML frameworks
  • R
  • SQL
  • Shell scripting
• Strong communication and stakeholder management skills, with the ability to work effectively with both technical and business teams.
• Excellent data visualization and presentation skills, with the ability to communicate complex analytical findings clearly.
• Strong team player with the ability to collaborate, review peer work, share knowledge, and contribute to continuous improvement.