Data Scientist - Intelligence Engine

At SingleInterface, we are redefining how multi-location brands connect with customers in an AI-driven digital world. Backed by $30M in funding from PayPal Ventures and Asia Partners, we are scaling our AI Retail Tech Platform to empower businesses with seamless digital presence management, AI-powered customer engagement, online reputation management, and store-level product & service integration. Our data-driven approach helps brands enhance customer experience, gain insights, and drive revenue growth.

Our Vision

Making AI-driven solutions simple & accessible for hyperlocal businesses to manage complex digital marketing needs.

Our Goal

Fuel growth for 10 million business locations by 2030 through advanced AI-powered frictionless experiences.

Our Core Values

•     Customer First – We put our customers at the heart of everything we do.

•     Getting Things Done – Speed and execution define our approach.

•     Being Authentic – We stay true to our people and mission.

•     Being Finicky – We obsess over the details that matter.

•     Being Techurious – We challenge the status quo with tech-driven solutions.

Role Summary

You’ll help build the Intelligence Engine: the learning layer of our platform that improves itself over time using data, experiments, and models. This role is for people who don’t just run notebooks — they ship outcomes.

Key Expectations

·      Build and improve foundational ML models powering discovery, ranking, relevance, and conversion signals.

·      Curate datasets (structured and unstructured), define labeling strategies, and own feature/model iterations end-to-end.

·      Run model experiments: offline evaluation, online experimentation (A/B), and rapid iteration loops.

·      Design evaluation frameworks for LLMs, embeddings, retrieval, and agent decisioning, including human-in-the-loop checks where needed.

·      Partner tightly with Product and Engineering to translate ambiguous problems into measurable model wins.

Technical Requirements (What you should be strong at)

·      Model training: classical ML and deep learning (PyTorch/TensorFlow), loss functions, regularization, calibration, bias/variance trade-offs.

·      Representation learning: embeddings, metric learning, retrieval, similarity search, vector databases (or equivalent).

·      LLM-related workflows: fine-tuning (as applicable), prompt and retrieval strategies, evaluations, hallucination checks, guardrails.

·      Ranking and personalization: learning-to-rank, recommender patterns, propensity models (bonus).

·      Experimentation: strong statistical thinking, causal intuition, offline-to-online translation, metric design.

·      Data fluency: SQL and Python, feature engineering, data quality checks, pipeline sanity.

·      Bonus: RL or bandits (explore-exploit), multi-agent evaluation or orchestration metrics.

Qualifications and Experience

·      2–6 years building and training ML models that made it to production and moved a business metric.

·      Strong fundamentals in ML, math, and statistics; you can reason about trade-offs, not just copy architectures.

·      Comfortable with ambiguity, fast iteration, and high ownership.

What’s on offer:

·      High-ownership role building a core “brain” for the platform.

·      Work with strong Product and Engineering teams, a fast-shipping culture, and real-world scale.

·      A platform for you to grow yourself with maximum speed with zero obstacles and make real impact.

·      We are an in-office first org — because no great idea ever started with “You’re on mute.” We thrive on hallway high-fives, spontaneous brainstorms, and the kind of teamwork that just hits different when you're in the room.

We believe in providing equal employment opportunities for all applicants. We appreciate diversity and believe that varied perspectives will make us better at what we do, so however you identify and whatever background you bring with you, we're excited to hear from you.