Senior Data Scientist

Roark Capital•Atlanta, GA

16h•Onsite

About The Position

Roark is growing its in-house Data and Analytics team with the goal of better leveraging advanced analytics, data science, and machine learning to make sound investment decisions and guide its owned businesses to profitable growth. This role is for a Senior Data Scientist to ship production-grade models and tools across Roark investment use-cases and its brands. The team is fast-paced, growing, and entrepreneurial, and this role will enjoy working across many topics and brands as they build durable competitive advantage in how they underwrite investments and scale portfolio performance. This is a hands-on, senior individual-contributor role for a generalist data scientist with strong MLOps and software-engineering depth. Typical work spans regression modeling, demand forecasting, customer and unit economics modeling, pricing and promotion analytics, classification problems, and GenAI productivity tooling. The individual will own models end-to-end — from framing the problem through deployment, monitoring, and maintenance, in addition to taking existing models built by others and transforming them into to be more reusable and/or put into production. The ideal candidate is a self-sufficient developer who raises the craft of the people around them, comfortable across a wide variety of tasks, gets up to speed on a new project quickly, and adds value in an agile fashion. They work in an 80/20 style and do not need the perfect dataset to get started. This role partners closely with investment teams, owned businesses, and Data & Software Engineering to deliver scalable, production-ready solutions that drive real business impact. This position is expected to be in-office to support collaboration and culture building.

Requirements

Bachelor’s degree in a quantitative discipline (computer science, data science, engineering, mathematics, statistics, economics, or similar), Master’s Degree preferred.
5–8 years of applied data science experience, operating with senior-level autonomy and requires minimal coaching or guidance
Demonstrated breadth: has shipped a variety of models; from forecasting, regression, and classification work, and is comfortable across the core algorithm families
Has owned at least one model in production — deployed, monitored, and maintained (not proof-of-concept only)
Strong software craft in Python: clean, tested, reusable code; Git; code review; and packaging
Cloud deployment experience (Google Cloud Platform preferred — Vertex AI, BigQuery, Cloud Run) and MLOps fundamentals you can teach others: CI/CD for ML, reproducibility, deployment, and monitoring
Evidence of having raised others’ engineering standards, not only your own
Client- and stakeholder-facing delivery experience, Either Consulting engagements or as cross-functional business support.
Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future; role is based in Atlanta, GA
Core areas of desired expertise include: Regression and classification across a range of business problems, Pricing and promotion analytics, Marketing Analytics, Customer and unit economics modeling, Experiment design and measurement, Forecasting and demand prediction, including unit-level forecasting
Tools and platforms: Python and SQL, Cloud platforms, Google Cloud Platform preferred (Vertex AI, BigQuery, Cloud Run), MLOps tooling: CI/CD for ML, reproducibility, deployment, and monitoring, Generative AI coding productivity tools (Claude Code, Cursor, etc.) – Candidate should be comfortable using AI tools to be a more efficient and stronger Data Scientist.

Nice To Haves

Generative AI / agentic development experience — Mastra, LangGraph, RAG, and LLM applications
Domain adjacency: QSR, multi-unit consumer, retail, CPG, or franchise businesses; unit-level forecasting
Orchestration and pipeline experience: Airflow, dbt, Dataflow
Has authored reusable IP — internal libraries, templates, or standards documentation
High standards of integrity, accountability, character, and professionalism
A self-sufficient Developer who delivers on time analysis and code
Raises the craft of those around them rather than only their own output
Hands-on, pragmatic, and results-oriented, with a bias for action and a willingness to roll up sleeves to get the job done
Comfortable working across a wide variety of tasks; gets up to speed on a new project quickly and adds value in an agile fashion
Works in an 80/20 style — does not need the perfect dataset to get started, and balances speed and impact with technical rigor
Thrives in a fast-paced, high-demand environment with multiple competing priorities; self-starter with a sense of urgency to deliver under tight deadlines
Low-ego team player who enjoys collaborating with high-intellect colleagues and stakeholders across various functions and backgrounds
Effective interpersonal and communication skills, with a demonstrated ability to collaborate with partners across different functions, backgrounds, and levels of seniority
Quick learner, high curiosity; strong problem-solving and conceptual skills
Entrepreneurial, agile mindset; someone who wants to take part in building a world-class analytics organization

Responsibilities

Own machine learning models end-to-end — build, deploy, monitor, and maintain them in production, including forecasting, regression, and classification, beyond proof-of-concept
Translate ambiguous business problems into well-defined modeling approaches and data requirements, working in an 80/20 style without waiting for a perfect dataset
Write clean, tested, reusable Python; participate in code review; and apply strong software craft, including Git and packaging
Deploy and operate models on the cloud (Google Cloud Platform preferred — Vertex AI, BigQuery, Cloud Run), applying MLOps fundamentals: CI/CD for ML, reproducibility, deployment, and monitoring
Raise the engineering and modeling standards of those around you — teaching craft and lifting team quality, not only your own output
Partner directly with investment teams, owned-business stakeholders, and internal clients to deliver analysis and tools that drive decisions
Author reusable IP — internal libraries, templates, and standards documentation — that scales across Roark’s businesses and target sectors
Get up to speed quickly on new projects and brands, adding value across a wide variety of tasks in an agile fashion
Ensure rigor in model validation, performance monitoring, and documentation, balancing sophistication with interpretability, speed, and impact
Stay current on advances in machine learning, AI, and applied analytics relevant to private equity and consumer businesses