Staff Applied Data Scientist

About SWARM
SWARM Engineering ("SWARM") is an AI-powered optimization platform that automates complex operational decisions across supply chain, workforce, production, and logistics. SWARM's AI-powered decision intelligence models customers' entire operations and surfaces the right decisions, before volatility steals or erodes margins.​ SWARM is a fully remote company with employees spread across the United States, and contractors worldwide.
Description
Key Responsibilities
Technical & Strategic Leadership

Architect End-to-End Solutions: Define the technical vision, architectural standards, and best practices for data science projects, ensuring models are robust, scalable, and maintainable in production environments
Drive Business & Operational Impact: Identify high-value opportunities to apply optimization, machine learning, and statistical modeling specifically to solve real-world operational planning challenges
Data Science Infrastructure & Innovation: Lead the development of internal data science infrastructure, tools, and workflows, while exploring side projects and emerging technologies to keep our technical stack cutting-edge
Team Management & Mentorship

Manage & Guide the DS Team: Provide technical leadership, coaching, and career mentorship to the data science team, fostering a culture of high performance, collaboration, and continuous learning
Resource & Project Planning: Work with leadership to scope, prioritize, and allocate team resources effectively across customer implementations and core product development
Code & Architecture Reviews: Oversee technical quality by conducting rigorous code reviews, establishing testing standards, and enforcing software engineering best practices across the team
Customer Engagement

Direct Customer Engagement: Actively participate in customer implementations to uncover root business problems, deeply empathizing with user workflows and operational constraints
Translate Requirements to Roadmap: Bridge the gap between non-technical stakeholders, business domain experts, and engineers by translating complex customer requirements into actionable data science deliverables
Validation & Value Realization: Collaborate directly with clients during deployment phases to test hypotheses, validate solution, and demonstrate quantifiable business ROI

Qualifications
Required

Degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Operations Research) or equivalent practical experience
Strong Software Engineering Foundation: Advanced programming skills in Python with ability to write modular, object-oriented code, and experience with relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, Pandas, NumPy)
Proven Track Record: Extensive experience applying data science and mathematical optimization to real-world operational planning, supply chain, or scheduling problems
Leadership Experience: Prior experience managing, leading, or mentoring data scientists and driving cross-functional alignment across product, engineering, and business stakeholders
Customer-Facing Aptitude: Excellent communication and consultative skills, with a track record of building trust directly with customers during technical implementations
Experience with data manipulation and cleaning using SQL and/or other data processing tools
Experience with cloud platforms
Strong problem-solving skills and a passion for data-driven decision-making

Even Better

Experience with modeling libraries like CVXPy, Gurobi, or Google OR-Tools
Experience with Microsoft Azure
Experience with CI/CD principles
Experience with Docker/Kubernetes
Experience with LLMs (OpenAI, Gemini)
Advanced proficiency or fluent in Spanish