Data Scientist
Summary
A data scientist consultant partners with business teams to identify problems, build ML/AI models, and deploy analytics solutions using Python/R, SQL, Spark, and cloud platforms like AWS.
Descriptions:
- Partner with business stakeholders to uncover operational challenges and convert them into clearly defined data science and artificial intelligence initiatives.
- Assess available data, project feasibility and potential business value to recommend suitable analytical approaches and deliverables.
- Prepare and transform datasets through data cleansing, preprocessing, feature creation and exploratory analysis.
- Develop statistical, machine learning and optimisation models that address identified business needs and support measurable outcomes.
- Collaborate closely with stakeholders throughout the project lifecycle to gather feedback, refine solutions and maintain delivery timelines
- Translate complex analytical results into clear business insights, compelling narratives and effective data visualisations for decision-makers
- Produce analytical solutions, including machine learning models, AI-enabled workflows, dashboards and automated data-processing pipelines.
- Work alongside engineering and product teams to embed data science capabilities into digital platforms and user-facing products.
- Establish model deployment, monitoring and maintenance processes in accordance with DevOps and ModelOps practices.
- Contribute within cross-functional agile teams by sharing technical expertise, resolving data-related challenges and promoting the practical use of analytics.
Requirements:
- Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Economics, Quantitative Social Science or a related discipline; relevant professional certifications may also be considered.
- Minimum 2 years of relevant experience across data science, marketing analytics and/or adtech.
- Strong knowledge of statistical analysis, feature engineering, machine learning and optimisation techniques, with proficiency in Python or R.
- Hands-on experience managing large datasets using technologies such as SQL or Spark, together with experience developing and deploying production-ready data science solutions.
- Familiarity with DevOps and ModelOps tools and practices, cloud platforms such as Amazon Web Services, and data platforms such as Databricks; strong stakeholder engagement, problem-solving and communication skills are essential.
- Effective stakeholder engagement and communication skills, with the ability to frame business problems analytically and work iteratively with users to deliver practical outcomes.
Clarence Khoh
R1552376