Data Scientist

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.