Data Scientist

Job Description

Roles & Responsibilities

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.

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