Purpose of the Job

As a key member of the Digital and Technology team, the Associate Manager Data Science, will be responsible to model complex problems, discover insights and automate workflows through statistical analysis, machine learning, generative AI and data visualization techniques using state of the art big data & AI technologies.

This involves co-creating data science initiatives with business, driving the adoption of advanced analytics and AI to achieve competitive advantage and operational excellence and support innovation.

The overall purpose of this role is to:

• Execute advanced data science projects, from conception to deployment, ensuring alignment with business objectives.
• Gain a thorough understanding and know-how of the on premise and cloud-based data warehouse and data lake.
• Continuously keeping updated with the latest AI and ML technologies.
• Building data pipelines to ingest data for Data Science projects.
• Data cleaning and data imputation of unclean data.
• Performing a thorough exploratory data analysis and presenting results to relevant stakeholders for insight generation.
• Feature engineering relevant to the purpose of the Data Science problem.
• Create and evaluate data models using statistical, algorithmic, data mining, deep learning, visualization, and data matching techniques for data discovery and intelligent automation.
• Present and summarize modeling results and recommendations to business users and stakeholders in an understandable and relevant way, verbally and visually.
• Document and publish analyses, instructions, and code so others can replicate and learn from previous work.

Job Description

Customer:

• Work closely with business stakeholders to identify challenges and opportunities, devise data-driven strategies, and implement solutions that leverage the latest in AI and machine learning technologies.

Process:

• Follow Standards set out and contribute to standards enhancement
• Ensure Documentation and Share knowledge within teams collaborating effectively with all

Ensure accuracy, consistency, and security.

Job Requirements - Experience and Education

• Bachelor’s or master’s degree in Computer Science or Engineering or Statistics, Applied Mathematics,

• 4+ years of experience in data science,

• Strong expertise in machine learning, statistical analysis, and predictive modeling, with hands-on experience using Python, R, Scala, or similar programming languages.

• Proficient in big data technologies and architectures, and cloud-based solutions (AWS, Google Cloud, or Azure).

• Demonstrated experience in developing and deploying scalable machine learning models in production environments.
• Expertise in statistical modeling, machine learning, NLP, and Generative AI, with the ability to translate these capabilities into business solutions.
• Experience with docker, containerization and continuous integration and deployment (CI/CD) tools such as AWS CodePipeline, Jenkins, Azure DevOps.
• Understanding of data engineering tools with respect to data lake services. (Glue, Glue Catalog, RedShift, Athena, Kinesis, S3, SNS, SQS, Matillion).
• Expertise in AWS or equivalent Google services – CloudFormation/ TerraForm/ CloudDeploymentManager, Lambda/GoogleCloud functions, API Gateway, SageMaker/AI Platform, ECS (Elastic Container Service)/CloudRun, VPC (Virtual Private Cloud), Security Groups, CloudWatch/CloudAlerting, and Step Functions/Workflows. Equivalent experience in similar cloud services platforms is also highly valued.
• Excellent communication skills, with the ability to convey complex data insights to both technical and non-technical audiences.
• Comfortable working in a fast-paced and highly collaborative environment. A great team player who embraces collaborations also work well individually while supporting multiple projects in parallel.

• Strong analytical and problem-solving skills, with the ability to interpret complex data and generate actionable insights.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests etc.) and experience deploying these in applications

Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and experience in deploying these in production environments

Leadership Behaviors

Skills