Lead Data Scientist – Synthetic Systems

About us :     Populix is a consumer insights platform that helps businesses connect with its database of respondents and provides them with insights to better understand the preferences of Indonesian consumers.

Populix has a pool of over 1,000,000 diverse, readily accessible, and highly qualified respondents across Indonesia.

Its products range from intensive research studies to simple surveys and can be arranged on a project or subscription basis.

Focusing on Indonesian consumers being super sticky to their phones, Populix facilitates a diverse range of data collection methods via its mobile app.

About the Role : Populix is building the future of AI-powered market research, combining structured data, unstructured insights, and generative AI into a seamless research intelligence platform.

We're looking for a Lead Data Scientist to help drive that vision forward, someone who can spearhead the development of simulation systems and automation pipelines, while actively supporting the Head of Data Science in shaping our AI research strategy.

This role will be at the forefront of building simulation modeling, scaling automation for text and audio-based survey data, and translating our research into whitepapers that position Populix as a thought leader in the region.

You'll also play a key role in advancing our use of retrieval-augmented generation (RAG) and modular AI architectures to deliver insights that are fast, accurate, and contextualized.

Key Responsibilities : Lead the design and implementation of behavioral simulation responses and demographic patterns using generative models, statistical modeling, and controlled simulations.

Collaborate with the research and marketing teams to create simulation-driven whitepapers and internal studies, helping communicate the value of synthetic insight across use cases like campaign testing, segmentation, and hypothetical trends.

Drive automation of research workflows that involve open-ended responses and audio data, including pipelines for transcription, classification, summarization, and sentiment analysis.

Work with the Head of Data Science to translate high-level product and research strategy into technical roadmaps, experiment plans, and model architecture decisions.

Help scale our AI insight engine by contributing to Retrieval-Augmented Generation (RAG) workflows and collaborating with LLM engineers on modular pipelines for context-rich output generation.

Collaborate closely with engineers, designers, and product teams to ship robust ML-powered tools into production across the Populix platform.

Provide mentorship to other data scientists, sharing knowledge, reviewing modeling work, and helping maintain a culture of experimentation, reproducibility, and ethical AI.

Required Qualifications : Master’s degree required, preferably in Computer Science, Statistics, Data Science, or a related quantitative field; PhD is a strong plus.

5+ years of experience in data science or applied machine learning, including at least 1 year in a technical leadership role.

Deep experience in generative modeling (e.g., GANs, VAEs), simulation, or behavioral data modeling, with a strong grounding in statistics and hypothesis testing.

Hands-on experience with Retrieval-Augmented Generation (RAG) architectures and knowledge integration with LLMs.

Solid programming skills in Python and experience with tools like LangGraph, LangSmith, scikit-learn, PyTorch, Hugging Face, or equivalent frameworks.

Familiarity with both structured (e.g., survey data) and unstructured (e.g., audio, text) data workflows, including preprocessing, feature extraction, and integration into insight pipelines Experienced in creating ideas and coding them into effective AI-driven solutions to real-world problems.

Strong communication skills and the ability to translate complex modeling approaches into product or research value.

Preferred Qualifications : Prior experience in market research, behavioral analytics, or social data modeling.

Exposure to speech processing, voice-to-text systems, and sentiment detection from audio or conversational data.

Knowledge of synthetic data generation ethics, validation strategies, and mixed-method evaluation.

Experience working with cloud-based analytics environments and orchestration tools (e.g., BigQuery, Airflow, Kubeflow, MLflow).

Experienced in working as an individual contributor.

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