Data Scientist — Data Lead, AI Products

Company Description Aleph-Labs is a creative engineering company headquartered in Singapore, serving clients across the Asia Pacific region with financial, internet, mobile, and desktop solutions. The team combines deep expertise in technology and design, enabling the company to bridge the gap between user experience and robust engineering. Having grown from a small garage operation to nearly 300 team members, Aleph-Labs maintains a strong, borderless culture that encourages collaboration across markets. The company is user-centric, focusing on building applications that people enjoy using and that clients are proud to own. Aleph-Labs thrives on solving complex problems and turning challenges into functional, flexible digital products.

Role Description The Data Scientist — Data Lead, AI Products is a full-time remote role responsible for guiding the data strategy and analytics for AI-driven products. This role involves designing and implementing data pipelines, performing exploratory and statistical analysis, and building predictive models to support product features and business decisions. The Data Lead will collaborate with product managers, designers, and engineers to define data requirements, translate business questions into analytical tasks, and ensure that insights are actionable and measurable. Day-to-day responsibilities include developing data visualizations and dashboards, validating model performance, overseeing data quality and governance, and mentoring other data professionals on best practices. The role also includes contributing to experimentation frameworks, documenting analytical methodologies, and communicating findings to technical and non-technical stakeholders.

What you will lead

Data strategy, architecture and quality

• Own the technical direction and delivery of the data layer, from raw ingestion through analysis and production use.
• Design data models and pipelines for extremely large, live, multimodal datasets, connecting structured records, documents, images, audio, video and sensor streams through consistent identities, timestamps and metadata.
• Establish standards for data quality, provenance, versioning and reproducibility, including robust handling of missing, inconsistent, delayed and duplicate data.
• Partner with infrastructure engineering on scalable storage, streaming, processing and access, and identify what additional data should be collected to improve the product.

Applied research and discovery

• Find meaningful connections across datasets and translate them into testable hypotheses and product opportunities.
• Lead statistical investigation and experimentation using methods such as forecasting, time-series analysis, classification, clustering and anomaly detection.
• Distinguish correlation from causal evidence, quantify uncertainty and validate promising research methods against practical baselines.
• Recommend which opportunities to pursue, what evidence is needed and when an approach should be stopped.

Production AI and data capabilities

• Turn validated findings into predictive models, data services, recommendations and intelligent workflows, including data foundations for retrieval, agent tools and multimodal AI.
• Own evaluation methods, acceptance criteria and monitoring for data and model quality.
• Partner with infrastructure and frontend colleagues to deliver enterprise capabilities that meet accuracy, latency, throughput, cost, privacy and access requirements.
• Monitor data and model drift, investigate failures and improve systems using real outcomes and user feedback.

AI-native, agentic ways of working

We build AI products, and we use AI extensively to build them.

You are expected to use coding agents and agentic workflows throughout research, data exploration, pipeline development, experimentation, testing and documentation. You should know how to break complex work into tasks, provide agents with appropriate context and tools, and validate their outputs.

As the data lead, you will also establish repeatable AI-assisted workflows that improve the team’s speed and quality.

You remain accountable for analytical correctness, engineering quality and the integrity of every result.

Strong technical judgment and rigorous verification are essential.

Required background and experience

• At least 5 years of relevant professional experience

  in applied data science, statistical modelling, data engineering or closely related work, includ…