New opportunity

Data Scientist (AI/ML)

Epergne Solutions · Singapore

About this role

Senior Consultant-level Data Scientist (AI/ML) role in Singapore, focused on production data science and machine learning models, supporting data infrastructure, and identifying business and operational analytics opportunities.

Responsibilities

Design and develop data pipelines and backend services for data ingestion, processing, and access; collaborate with data engineering teams on pipeline design and analytics layers.
Build, deploy, and maintain production-ready data science and ML models. Analyze data to identify analytics, AI, and ML use cases, and support hypothesis testing and model validation.
Make data models and outputs accessible and meaningful to downstream applications. Document model development, pipeline architecture, and technical decisions, and contribute to team knowledge sharing and capability development.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, plus at least 8 years of data science and machine learning experience.
Proven experience developing and deploying ML models in production; strong Python and SQL skills; experience with modern data platforms such as Databricks or equivalent, and AWS or other cloud-based data and analytics platforms.
Knowledge of data pipelines, analytics architectures, and MLOps practices.

Advantages: Familiarity with IoT, telemetry, sensor, or industrial data processing; experience supporting generative AI, LLM, or RAG workloads.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Data ScienceMachine LearningPythonSQLDatabricksAWSData PipelinesData EngineeringMLOpsHypothesis TestingModel ValidationCloud AnalyticsInternet of Things (IoT)TelemetrySensor Data ProcessingIndustrial Data ProcessingGenerative AILarge Language Models (LLMs)Retrieval-Augmented Generation (RAG)

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Production ML delivery
The role explicitly covers building, deploying, and maintaining production-ready data science and ML models. Prepare to explain decisions across model development, deployment, validation, and ongoing maintenance.
Pipelines and data access
The posting calls for designing data pipelines and backend services for ingestion, processing, and access, in collaboration with data engineering. Prepare a concrete example of how you defined pipeline responsibilities and an analytics layer.
Useful business and operational analytics
The role includes identifying AI and analytics opportunities, supporting hypothesis testing and model validation, and making outputs meaningful to downstream applications. Prepare to show how you connected a use case to a testable hypothesis and usable output.
Technical documentation and team capability
The Senior Consultant-level role explicitly includes documenting model development, pipeline architecture, and technical decisions, as well as contributing to knowledge sharing. Prepare examples of how you made decisions understandable and reusable by others.

Likely challenges to think through

Balancing model performance with production reliability
A likely trade-off, not a stated employer problem, is choosing a model that performs well in validation while remaining practical to deploy and maintain. Prepare an example that explains the validation approach, production constraints, and resulting choice.
Coordinating data boundaries with engineering teams
Because the role spans pipelines, backend services, and collaboration with data engineering, ownership of ingestion, analytics layers, and model inputs may require careful coordination. Prepare to describe how you would agree on interfaces, data quality expectations, and technical decisions.
Turning promising use cases into usable outputs
An inferred difficulty is that an analytically sound result may not be meaningful or accessible to a downstream application. Prepare to explain how you would test a business or operational hypothesis, validate the model, and define the output for its intended use.

Practice your answers

Technical practice

How would you design a data pipeline and backend service for ingestion, processing, and access?

What to coverThe role includes designing pipelines and backend services. Suggested preparation: explain data flow, failure handling, data quality checks, and how consumers would access processed data.

How would you use Python and SQL to prepare data for an ML model?

What to coverThe posting asks for strong Python and SQL skills. Suggested preparation: discuss where you would perform transformations, how you would check correctness, and how you would address slow queries or processing jobs.

What would you consider when choosing an analytics architecture on a modern cloud data platform?

What to coverThe role involves analytics layers and platforms such as Databricks or an equivalent, alongside AWS or another cloud platform. Suggested preparation: compare approaches in terms of data access, maintainability, performance, and collaboration with data engineering.

How would you identify and prioritize an analytics or ML use case?

What to coverThe role calls for finding business and operational opportunities in data. Suggested preparation: explain how you would frame the decision, assess available data, establish a baseline, and decide whether ML adds value.

How would you structure hypothesis testing for an operational use case?

What to coverHypothesis testing is an explicit responsibility. Suggested preparation: define the hypothesis and outcome measure, address confounding or selection bias, and explain what result would change a decision.

How would you validate an ML model before it is used in production?

What to coverThe posting calls for model validation and production-ready models. Suggested preparation: cover representative data splits, suitable metrics, leakage checks, and performance across relevant data segments.

How would you deploy and maintain a production ML model?

What to coverProduction deployment, maintenance, and knowledge of MLOps practices are stated requirements. Suggested preparation: describe reproducible releases, monitoring for data or performance changes, and a response plan when results degrade.

How would you make model outputs useful to downstream applications?

What to coverThe role requires accessible, meaningful data models and outputs. Suggested preparation: explain output definitions, interface or data-contract choices, versioning, and how you would verify that consumers interpret results correctly.

What challenges would you anticipate with telemetry or sensor data?

What to coverIoT, telemetry, sensor, and industrial data familiarity is listed as an advantage, not a core requirement. Suggested preparation: if relevant, discuss missing readings, timestamps, changing data rates, and how these affect processing or validation.

How would you evaluate whether an LLM or RAG approach fits a use case?

What to coverExperience supporting GenAI, LLM, or RAG workloads is listed as an advantage. Suggested preparation: compare the approach with simpler alternatives and discuss retrieval quality, evaluation, and operational trade-offs without assuming a specific project.

Behavioural practice

Tell me about a time you worked with data engineers on a pipeline or analytics layer.

What to coverThe role explicitly calls for this collaboration. Suggested preparation: use a real situation, your actions to resolve design or ownership questions, and the resulting outcome.

Describe a time you handled a problem with a deployed model or its data.

What to coverMaintaining production models is part of the role. Suggested preparation: give the situation, how you investigated and communicated the issue, what you changed, and the result.

Tell me about a time you challenged or refined a proposed analytics use case.

What to coverThe role involves identifying opportunities and supporting business and operational analysis. Suggested preparation: explain the initial request, how you assessed evidence and trade-offs, your actions, and the decision that followed.

Describe a time you made technical outputs understandable to downstream users.

What to coverThe posting emphasizes accessible and meaningful model outputs. Suggested preparation: describe the audience and misunderstanding risk, what you changed or explained, and the observed result.

Tell me about a time your documentation or knowledge sharing helped a team.

What to coverDocumenting technical decisions and developing team capability are stated responsibilities. Suggested preparation: describe the situation, what you documented or taught, and how you know it helped.

Sharpen your knowledge

Topics to prepare

Production ML lifecycle
The posting requires building, deploying, and maintaining production-ready models, plus knowledge of MLOps. Revise how model versions, monitoring, and retraining decisions fit together; sketch a lifecycle for a model whose input data changes over time.
Data pipelines and analytics architecture
The role designs ingestion, processing, and access pipelines with data engineering teams. Trace a dataset from ingestion through an analytics layer to a downstream application, identifying where data quality checks and ownership decisions belong.
Hypothesis testing and model validation
The posting explicitly calls for both in business and operational use cases. Work through a proposed use case: state a testable hypothesis, choose a validation approach and metrics, and explain what result would change the decision.
Turning data into usable outputs
The role identifies analytics, AI, and ML opportunities and makes outputs meaningful to downstream applications. Compare two possible use cases and explain which warrants a model, what output it should provide, and how its meaning would be communicated.

Skills to revise

Python pipeline and service implementation
Python and pipeline/backend development are explicit requirements. Practice writing a small ingestion-and-processing flow that handles malformed records and exposes a documented output for another application.
SQL for analytics layers
Strong SQL skills and analytics-layer development are specified. Write queries that transform raw records into a reusable analytical dataset, then check joins, duplicates, and missing values.
Model deployment and maintenance walkthrough
Production deployment experience is required. Rehearse a concrete deployment walkthrough: validation before release, how predictions are made accessible, how performance is checked afterward, and when a model would be updated.
Cloud data-platform design
The posting names Databricks or an equivalent modern data platform and AWS or another cloud-based data and analytics platform. Using a platform you know, draw a pipeline-to-model workflow and explain its data access, deployment, and operational trade-offs without assuming a particular employer stack.
Technical decision documentation
The role requires documenting model development, pipeline architecture, and decisions. Draft a one-page design note for the workflow above covering data flow, model validation, downstream outputs, and the reasons for key choices.

Your preparation checklist

Map evidence to the stated requirements
Prepare a one-page reference to actual examples of production ML deployment, Python, SQL, a modern data platform, cloud analytics, pipelines, and MLOps. Note how your education and experience relate to the posting’s degree and 8+ years requirements; leave gaps explicit.
Prepare a production-model walkthrough
Select a real model you worked on and explain the use case, your contribution, deployment, maintenance, validation results, and technical decisions. The role explicitly calls for building, deploying, and maintaining production-ready models.
Sketch a pipeline and service design
As a practice exercise, draw how data could be ingested, processed, and made accessible through pipelines, an analytics layer, and backend services. Annotate where Python, SQL, a platform such as Databricks or equivalent, and cloud services would fit, without assuming the employer’s stack.
Work through a use-case and validation example
Prepare a concise example of turning a business or operational question into an analytics or ML use case, with a hypothesis, validation approach, and criteria for interpreting the result.
Show how outputs reach downstream users
Prepare an example, if available, of making model outputs understandable and usable by an application. Bring a brief account of the documentation, architecture decisions, and collaboration with data engineers involved.
Review the advantageous domains selectively
If you have relevant work, prepare a short example involving IoT, telemetry, sensor or industrial data, or GenAI, LLM or RAG workloads. The posting describes these as advantages, not core requirements.
Write questions for the employer
Ask which business or operational use cases are priorities, how data engineering and data science divide pipeline ownership, what production-model maintenance involves, and which data platform and cloud services the team uses.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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