What You'll Do:
- Partner with business teams to understand challenges, clarify objectives, and define analytically sound problem statements. Translate business needs into actionable data science projects.
- Clean, preprocess, and transform structured and unstructured data. Engineer features that improve model performance and integrate multiple data sources for comprehensive analysis.
- Build predictive, classification, clustering, or other machine learning models. Evaluate performance, iterate based on results and stakeholder feedback, and ensure models meet business objectives.
- Present findings through clear narratives, dashboards, and interactive tools. Translate complex analytical results into actionable recommendations for decision-making.
- Design intuitive dashboards, visualizations, and self-service analytics tools to help teams monitor metrics, explore data, and act on insights.
- Collaborate with engineering teams to operationalize models. Monitor performance, recommend enhancements, and support iterative improvement in production workflows.
What We're Looking For:
- Strong analytical skills with the ability to convert business questions into data-driven solutions.
- Proficiency in Python, R, SQL, and libraries such as pandas, scikit-learn, or equivalents.
- Solid foundation in statistics, probability, hypothesis testing, and exploratory data analysis.
- Experience building and validating machine learning models using modern frameworks.
- Familiarity with data engineering concepts, including data lakes, warehouses, marts, REST APIs, and big data tools (Spark, Hadoop, Kafka).
- Expertise in data visualization tools and frameworks such as Tableau, Power BI, Plotly, or custom dashboards.
- Strong communication skills, capable of conveying insights to both technical and non-technical audiences.
- Collaborative mindset, highly organized, and comfortable working in Agile environments with multiple priorities.
Qualifications & Experience:
- Degree or equivalent experience in Data Science, Statistics, Computer Science, Engineering, Mathematics, or related fields.
- Proven experience managing end-to-end data science projects-from scoping and data prep to modeling, visualization, and deployment.
- Prior experience working with cross-functional teams and stakeholders in a fast-paced, iterative environment.
- Hands-on experience with production-grade machine learning workflows is a plus.
Interested candidates are encouraged to submit their resumes outlining their relevant experience and achievements to apply88(@)talentvis.comor click apply!
..We regret to inform that only shortlisted candidates would be notified..
EA License No: 04C3537
EA Personnel No: R22106683
EA Personnel Name: Yang Hui Shan, Sherri
