Data Scientist (AI Engineer)

JOB OVERVIEW:

The Data Scientist (AI Engineer) serves as the critical technical bridge between high-level business strategy and scalable AI production. This role is dedicated to accelerating demand creation by translating complex client challenges into feasible, high-impact AI/ML prototypes and Proof of Concepts (PoCs).

By balancing technical rigor with strategic communication, the incumbent assesses data maturity, designs agile solutions using both traditional and Generative AI, and ensures that all innovations are architecturally sound and ready for production handover. Ultimately, this role drives organizational growth by de-risking AI investments and demonstrating the tangible technical value of emerging technologies.

Core Focus Areas

• Technical Translation: Converting business pain points into rigorous technical requirements and feasible AI architectures.

• Rapid Innovation: Designing and iterating on interactive prototypes that showcase the art of the possible while adhering to vetted Technology Readiness Level (TRL) standards.

• Strategic Advisory: Evaluating client data infrastructure and providing technical roadmaps to bridge the gap between current capabilities and AI readiness.

• Seamless Integration: Partnering with Production and Enablement leads to ensure prototypes are scalable, documented, and transitioned without technical debt.

• Technology Scouting: Researching and applying cutting-edge tools from the internal Tech Radar to solve real-world client problems.

DUTIES AND RESPONSIBILITIES:

Technical Requirements Elicitation & Analysis:

• Collaborate with AI/ML Business Analysts and clients to deeply understand their existing technical landscape, data infrastructure, and specific operational challenges.

• Translate high-level business needs into technical requirements for AI/ML solutions, identifying necessary data inputs, system integrations, and potential technical constraints.

• Assess the technical feasibility of proposed AI/ML use cases, ensuring alignment with the organization's vetted tech stack and TRL standards, given the client's current data capabilities and technology stack.

AI/ML Solution Conceptualization & Prototyping:

• Design and develop rapid, interactive prototypes or proof-of-concepts (PoCs) using appropriate AI/ML techniques (both traditional ML and cutting-edge AI like Generative AI).

• Demonstrate the functionality and potential business impact of AI/ML solutions through compelling technical demonstrations and visualizations.

• Iterate rapidly on prototypes based on client feedback, showcasing agility and responsiveness.

Technical Communication & Value Articulation:

• Simplify complex AI/ML concepts, algorithms, and technical architectures into understandable terms for non-technical business stakeholders.

• Clearly articulate the technical value proposition of proposed solutions, explaining how the AI/ML will work and why it is the right approach for the client's problem.

• Present technical findings, prototype results, and architectural considerations to clients and internal teams.

Organizational Data Capability Assessment (Technical):

• Conduct technical assessments of client data maturity, including data quality, accessibility, governance, and existing infrastructure.

• Advise clients on technical prerequisites and potential roadmaps for enhancing their data capabilities to support AI/ML initiatives.

Emerging Technology Research & Application:

• Actively research and evaluate new AI/ML technologies, tools, and frameworks (e.g., specific algorithms, cloud AI services, MLOps tools) relevant to demand creation and prototyping.

• Support the AI Solution Enablement Lead by exploring specific application-layer use cases for technologies already on the internal Tech Radar. Provide field feedback on the performance of vetted tools in client prototypes

• Identify how emerging technical capabilities can be applied to solve client problems and create new service offerings.

• Contribute technical insights to market exploration and strategic planning.

Cross-Functional Collaboration & Handover:

• Work in close partnership with the Data Science / AI Production Team from the earliest stages to ensure that prototyped solutions are designed with production scalability and maintainability in mind.

• Collaborate primarily with the AI Solution Enablement Lead to transition validated TRL 3 prototypes into the hardening phase. Ensure all initial technical elicitation is documented according to Enablement Quality Gate standards to prevent technical debt during handover

• Facilitate smooth technical handovers of validated concepts, prototypes, and initial architectural considerations to the production team, minimizing rework and ensuring continuity.

• Provide technical context and support during the transition phase to ensure the production team has a comprehensive understanding of the solution's intent and design.

• Problem Solving & Innovation:

• Apply strong analytical and problem-solving skills to technical challenges encountered during the demand creation and prototyping phases.

• Propose innovative technical approaches to address client needs and differentiate our solutions.

QUALIFICATIONS AND EXPERIENCES:

• Minimum Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field. Master's or Ph.D. preferred.

• At least 5+ years of progressive experience in AI/ML solution design, data science, or AI engineering, with a significant portion in a client-facing or consulting capacity.

• Proven experience in designing, developing, and deploying functional AI/ML prototypes that demonstrate business value and are engineered for production.

• Expert proficiency in Python and extensive experience with core AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Demonstrated hands-on experience with Generative AI and Large Language Models (LLMs), including frameworks like LangChain, for designing and prototyping novel applications.

• Strong practical experience with Google Cloud Platform (GCP) services relevant to AI/ML (e.g., Vertex AI, BigQuery, Cloud Storage, Dataflow, Cloud Functions).

• Proficiency with Docker for containerization and a foundational understanding of container orchestration (e.g., Kubernetes concepts for GKE).

• Solid understanding of MLOps principles for reproducibility, experiment tracking (e.g., MLflow, Vertex AI ML Metadata/Model Registry), and model versioning.

• High-level expertise in data governance frameworks, data quality assessment (including tools/libraries like Great Expectations), metadata management (e.g., Google Cloud Data Catalog), and data architecture principles.

• Strong expertise in distributed computing and parallel processing techniques is vital for handling large-scale AI workloads.

• A proven track record of innovations and executions in deep learning, demonstrated through shipping products or first-author publications at leading AI conferences, is a strong differentiator.

• Experience in the container shipping industry or related logistics/supply chain domains is highly advantageous.

• Excellent presentation, documentation, and stakeholder management skills.