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 [Confidential Information]:
- 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.
