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, including leadership of a substantial data workstream or platform.
- A strong academic foundation in Statistics, Applied Mathematics, Operations Research or a related quantitative discipline , with depth in probability, statistical inference, linear algebra, optimization and experimental design.

- Robust Python and SQL, with experience writing maintainable, tested production code and processing large-scale, streaming or continuously updated datasets.
- Experience connecting structured and unstructured data,



and delivering data or ML capabilities from messy source data through evaluation into production use.

- Sound judgment in model evaluation, including data leakage, overfitting, uncertainty and drift, supported by evidence of making architectural decisions and owning delivery.
- Established use of AI coding agents and agentic workflows in professional work, with rigorous verification of their outputs.

- The independence to turn ambiguous goals into a prioritized technical plan, communicate findings and trade-offs clearly, and carry work through to completion.

Especially valuable

- Depth in time-series analysis, spatial statistics, Bayesian methods, causal inference or signal processing.
- Experience with environmental, geospatial, agricultural, industrial, sensor or operational datasets.

- Experience building enterprise products for international users.
- Applied research that led to a deployed capability or a consequential product decision.
- Strong quantitative research, open-source contributions or recognized mathematical competition achievement.

- Experience setting technical standards and reviewing the work of colleagues or external contributors.

A PhD is not required.

What success looks like

You will be the person the team relies on to judge whether the data is trustworthy, whether a finding is defensible and whether a capability is ready for production.
- Data arrives reliably and remains connected, traceable and usable at scale.
- Technical decisions are clear, documented and appropriate to the product’s requirements.
- Research produces measurable learning and useful product capabilities.
- Models are evaluated against meaningful baselines and monitored after deployment.
- Enterprise users can rely on the quality and consistency of the product.
- Risks and limitations are surfaced early, with a clear plan to address them.
- AI-assisted execution delivers speed without sacrificing rigour.

What we would like to see Share one or two examples where you led the data work behind a production product or operational system. Explain the scale and complexity of the data, the decisions you owned, the methods you evaluated and the measured results.

Include an example of a connection or opportunity you discovered through analysis and how you established whether it was useful.

We would also like to understand how you use AI agents: what you delegate, how you verify their work and how those workflows improve delivery.

Work arrangement

This is a full-time remote role within India, with occasional company-paid travel for field understanding, planning and collaboration.

📌 Data Scientist — Data Lead, AI Products (India)
🏢 Aleph-Labs
📍 India