• Responsible for the design and implementation of machine learning and artificial intelligence solutions in analytics environments under the Customer Value Management (CVM) platform.

• Carry out data mining and statistical modelling for various machine learning use-cases.
• Search and select appropriate data sets before performing data collection and data modelling.
• Train and retrain machine learning systems and models as needed.
• Work closely with data engineers, business analyst, statisticians and subject matter experts to drive deep analysis and uncover useful insights.
• Identify differences in data distribution that could affect model performance in real-world situations.

• Perform data visualization for deeper insights.
• Analyze the use cases of machine learning algorithms and ranking them by their success probability.
• Explain and infer the results of various algorithmic approaches and evaluating their performance.
• Design systems, alerts, and dashboards to monitor machine learning workflows.
• Document machine learning processes.
• Ensuring that machine learning algorithms generate accurate CVM recommendations.

• Work with campaign management team to understand customer needs and develop proactive and innovative data driven solutions and campaigns.
• Conduct analyses with focus on experimental design, assessment, execution, and measurement of current marketing programs.
• Contribute and participate in campaign idea generation meetings and cross functional Customer Lifecycle Management meeting as required

• Work closely with CVM stakeholders to understand and maintain focus on their analytical needs, including identifying critical metrics and KPIs, and deliver actionable insights to relevant decision makers.
• Summarize analytic findings and integrate with non-traditional data sources when appropriate, to enhance campaign development initiatives.

• First degree in Mathematics, Statistics, Computer Science, Engineering or other related disciplines
• Fluent in English

• 1 – 2 years’ experience in AI/ML analyst roles
• Advanced proficiency with Python, SQL and PySpark
• Understanding of big data technologies
• Experience in aggregating & transforming data, exploring & manipulating data, creating training & inference pipelines, building & validating models.
• Extensive knowledge of ML frameworks, libraries, data structures, data modelling, and software architecture

• Certification in machine learning, data engineering, data science or related fields will be an added advantage
• Solid understanding of predictive analysis: predictive modelling, machine learning and data mining.
• Good understanding of customer data analysis, propensity modelling and segmentation techniques; excellent understanding of data manipulation and interrogation techniques
• Strong analytical, problem-solving and teamwork skills
• Good oral & written communications skills
• Experience working in a medium organization