Imagine decision-makers uploading data and, within minutes, receiving clear, actionable insights delivered straight to their fingertips. That is the promise of the AI Data Scientist, an autonomous Agent powered by large language models (LLMs) that closes the gap between evidence and action. Rather than simply writing code or responding to prompts, it reasons through questions, tests ideas, and delivers end-to-end insights at a pace far beyond traditional workflows. Guided by the scientific tenet of the hypothesis, this Agent uncovers explanatory patterns in data, evaluates their statistical significance, and uses them to inform predictive modeling. It then translates these results into recommendations that are both rigorous and accessible. At the core of the AI Data Scientist is a team of specialized LLM Subagents, each responsible for a distinct task such as data cleaning, statistical testing, validation, and plain-language communication. These Subagents write their own code, reason about causality, and identify when additional data is needed to support sound conclusions. Together, they achieve in minutes what might otherwise take days or weeks, enabling a new kind of interaction that makes deep data science both accessible and actionable.

First, The Data! • Over 1 million unique chat users • Over 150 million conversation data points focused on mental health and emotional wellbeing • Over 1.1 million manually labeled data points by our highly trained moderators • Over 1.2 million data points on moods and emotions • Over 480 thousand resources and services matched in real-time (patented) • Over 10 real-time AI engines Our own corpus and language models (patented) • Double feedback loop from users and moderators for ML and fine tuning • No hate/racism/trolls or other biases in the data, as all user chats supervised by our moderators Supportiv is driven by building a peer-to-peer mental well-being platform that helps users with their emotional needs 24/7/365 affordably. We are seeking a highly-analytical impact-oriented Ph.D. student to join our Data Science team. The role will serve as a core member within the team, focused on applying artificial intelligence (AI) to solve real business problems, along with analytics to drive insights from the user interactions on the platform to guide data-driven product development and business decisions. The Ph.D. student will work cross-functionally between various teams at Supportiv. Please include your resume and transcripts.

First, The Data! • Over 1 million unique chat users • Over 150 million conversation data points focused on mental health and emotional wellbeing • Over 1.1 million manually labeled data points by our highly trained moderators • Over 1.2 million data points on moods and emotions • Over 480 thousand resources and services matched in real-time (patented) • Over 10 real-time AI engines Our own corpus and language models (patented) • Double feedback loop from users and moderators for ML and fine tuning • No hate/racism/trolls or other biases in the data, as all user chats supervised by our moderators Supportiv is driven by building a peer-to-peer mental well-being platform that helps users with their emotional needs 24/7/365 affordably. We are seeking a highly-analytical impact-oriented Ph.D. student to join our Data Science team. The role will serve as a core member within the team, focused on applying artificial intelligence (AI) to solve real business problems, along with analytics to drive insights from the user interactions on the platform to guide data-driven product development and business decisions. The Ph.D. student will work cross-functionally between various teams at Supportiv. We are not just a highly ambitious technology company with a soul.

According to Gartner's 2019 CIO Survey, AI adoption by businesses grew 270% over the last four years, and over 37% of businesses have implemented AI in some facet. Businesses are adopting the technology at staggering rates, and Chief Information Officers and data scientists are facing difficult decisions regarding which speed of AI fits their business needs. AI can be broken down into three scoring patterns: batch, event-driven, and real-time. Each scoring pattern provides different capabilities, depending on the goal of the model. For example, while batch computing may work ideally in a payroll setting, it would not be an effective way to track fraud in banking transactions.