Assistant Data Scientists

The Data Scientist works closely with business stakeholders to uncover challenges, refine use cases, and design analytical solutions that drive measurable outcomes. This role spans the entire data science lifecycle—problem framing, data preparation, modelling, evaluation, communication of insights, and enabling adoption through compellingvisualizations and decision‑support tools.

Key Responsibilities

1. Problem Definition & StakeholderEngagement

• Facilitate discussions with business users to understand their pain points, clarify objectives, and convert them into analytically sound data science problem statements.
• Collaborate with stakeholders throughout the project to refine hypotheses, validate findings, and align on priorities.
• Manage expectations and timelines while balancing analytical depth with business needs.

2. Data Preparation & FeatureEngineering

• Conduct data cleaning, preprocessing, and transformation for both structured and unstructured data to ensure high data quality.
• Perform feature engineering to extract meaningful attributes that enhance model performance.
• Identify relevant datasets and integrate multiple data sources to support analytical work.

3. Analytical Modelling &Experimentation

• Explore data using statistical techniques to uncover trends, patterns, and relationships.
• Build machine learning models for prediction, classification, clustering, or other analytical tasks as required by the use case.
• Evaluate model performance and iterate based on stakeholder feedback and performance metrics.

4. Insights Delivery & Communication

• Translate analytical results into actionable recommendations that stakeholders can apply to drive decisions or business improvements.
• Develop clear, compelling narratives that explain insights and model behaviours in an accessible manner.
• Present analysis with well‑designed visuals, dashboards, and interactive tools that support storytelling and data exploration.

5. Visualisation & User‑Facing Tools

• Design dashboards and interactive visualizations to facilitate self‑service analytics and real‑time data monitoring.
• Apply strong visualization principles to ensure insights are intuitive and impactful.
• Build and deploy customised visual interfaces tightly integrated with underlying data systems.

6. Deployment, Monitoring &Continuous Improvement

• Support model deployment into production environments and collaborate with engineering teams to operationalize models.
• Monitor model performance over time and recommend enhancements as required.
• Contribute to agile project workstreams and participate in ceremonies such as stand‑ups, reviews, and retrospectives.

Required Skills & Competencies

Analytical & Technical Skills

• Ability to convert business challenges into analytical questions and identify appropriate data sources.
• Proficiency in writing data preparation and analysis scripts using tools such as Python, R, SQL, pandas, or equivalent.
• Strong foundation in descriptive statistics, probability, data exploration, and hypothesis testing.
• Skilled in building and validating machine learning models, using modern techniques and frameworks.
• Solid understanding of system design concepts, data structures, and algorithms.

Data Visualization & Storytelling

• Strong command of data visualization principles and tools.
• Capable of developing dashboards and interactive visual tools (e.g., using Tableau, Power BI, Plotly, or custom frameworks).
• Ability to communicate analytical insights through structured storytelling tailored for decision‑makers.

Data Engineering Familiarity

• Understanding of data modelling, data access patterns, and data storage structures such as data lakes, data marts, and data warehouses.
• Familiarity with REST APIs, web protocols, and data extraction via web scraping technologies.
• Comfortable working with big data frameworks (e.g., Spark, Hadoop, Kafka) when required for large-scale processing.

Soft Skills

• Strong communication skills, both written and verbal, with the ability to influence non‑technical audiences.
• Effective stakeholder management and experience working in iterative cycles with feedback loops.
• Organized, self-driven, and able to manage multiple priorities in an agile environment.

Experience Requirements

• Proven experience conducting end‑to‑end data science projects—from scoping and data preparation to modelling and deployment.
• Experience collaborating with diverse teams and managing stakeholders across business and technical functions.
• Prior experience in agile project management or participating in agile delivery teams.

Qualifications

• Degree or equivalent experience in Data Science, Statistics, Computer Science, Engineering, Mathematics, Information Systems, or related fields.
• Experience with production‑grade machine learning workflows is an advantage.

Please send your details in MS Word format to [email protected]:

- Position applying for

- Current remuneration

- Expected remuneration

- Notice period

John Goh Meng Chye

EA License No : 06C4642

EA Reg No : R1102621

We regret that only shortlisted candidates will be notified.