Lead Consultant, Applied Analytics

Key Skills

Aartificial intelligence, AWS, Azure, Data Analytics, Data Governance, Data management, Databricks, Machine learning techniques, MLOps tools, PySpark, R Programming, SDLC

Job Description

Key Roles and Responsibilities:

As our Analytics Lead in the forefront of AI/ML and BI delivery, you will be:

• Coaching and growing a team of Data & AI engineers, guiding them in their work to deliver impactful insights and best practices to deliver data-driven solutions.
• Developing and promoting best practices in analysis, information hierarchy and ML engineering techniques.
• Curating playbooks, standardized framework, practice guides and other artefacts that will allow our practitioners perform their work in a structured, standard manner.
• Creating and delivering narratives that are rich in insights, data rigor and recommendations tailored to any data customer up to and including C-suite executives.
• Identifying opportunities to engage prospective and existing clients on enhancing or integrating AI/ML into new or existing solutions.
• Leading Analytics workstreams in large scale projects to design, develop and deploy AI/ML and BI use cases.

Key Qualifications

You are a seasoned analytics practitioner with:

• 8+ years of end-to-end project implementation experience in delivering enterprise-level analytics solutions, ideally with a University degree in Computer Science or equivalent.
• Presales experience developing proposals and closing deals above SGD1 million, where analytics platforms, Model-as-a-Service and data engineering form a significant portion of the solution.
• Extensive experience in managing scope and risks in the SDLC of delivery application enhanced with AI/ML and/or BI capabilities.
• Strong detail-orientation and a keen sense of technology trends that could lead to business opportunities.
• Strong hands-on experience in python coding, MLOps and an enterprise BI tool such as Qlik, Tableau or Power BI.
• Strong design and implementation skills in logical and physical data models for analytics applications.
• Advanced knowledge of performance optimization in AI/ML models and computations.

You also might have hands-on experience in:

• Delivering AI/ML solutions in an Agile approach with clear approaches for risk mitigation.
• Enterprise analytics solutions such as Databricks, or coding languages such as R or PySpark.
• Leveraging on various data & analytics toolkits within cloud services like AWS or Azure.
• Data Management, Data Governance or real-time streaming technologies.