Data Scientist

ELLIOTT MOSS CONSULTING PTE. LTD.; Penarth, High Street; On-site; GBP 60,000 - 90,000; Full time

Job summary

Elliott Moss Consulting Pte. Ltd. is seeking a Data Scientist to join its Data Science and AI team, tackling strategic housing-related challenges with data-driven solutions. You will work with stakeholders across IT and business units to deploy ML/AI models on AWS, applying MECE problem-solving and end-to-end ML lifecycle practices.

The role emphasizes collaboration, rigorous data engineering, and clear communication of insights to non-technical audiences.

Qualifications

• 5+ years of professional experience in Data Science, ML, or AI.
• Bachelor's or master's in quantitative field.
• Strong MECE, root cause analysis, and hypothesis-driven problem solving.
• Experience deploying ML/AI in AWS.
• Knowledge of ML/DL algorithms and architectures.
• Hands-on AI engineering with LLMs, Generative AI, vector databases.
• Data engineering for ML including ELT concepts.
• Strong programming in Python, R, SQL, and Shell.
• Excellent communication and stakeholder management.

Responsibilities

• Contribute to strategic Data Science and AI projects using advanced ML techniques.
• Identify opportunities with stakeholders and translate into data-driven solutions.
• Develop, validate, deploy ML/AI algorithms and MLOps pipelines.
• Ensure data quality, efficiency, and scalable solutions.
• Review peers' work and uphold technical standards.
• Collaborate with IT, data engineering, and business units.
• Present findings and insights to technical and non-technical audiences.
• Stay current with AI developments and best practices.

Skills

Python

R

SQL

Shell scripting

MLOps

AWS

LLMs

Generative AI

Vector databases

LangChain/LangGraph

Deep learning

Statistical modelling

Optimization

Transformer architectures

Reinforcement Learning

Education

Bachelor's or master's in a quantitative field

Tools

LangChain

LangGraph

Vector databases

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.