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View current openings →Join Our Team as Impact Engineer - Python Back-end (Remote)
At Hyperion360, we believe in empowering our engineers to shape the future of technology from the comfort of their own homes. We are a premier software outsourcing company, partnering with some of the world’s most successful businesses to build and manage dedicated, remote teams of top-tier software engineers and other technical talent.
We are looking for a data scientist/analyst who thrives in a fast-paced, high-ownership environment
About This Role
We’re looking for a data scientist/analyst who thrives in a fast-paced, high-ownership environment. At our company, data is at the core of our decision engine, risk models, and product strategy. You’ll design, implement, and productionize data-driven systems that directly shape how we underwrite advances, optimize user experience, and manage risk.
This role is not limited to notebooks. You’ll write production-ready Python code, deploy models and pipelines into live systems, and collaborate with backend engineers to build scalable data products. You’ll also leverage modern AI/LLM-powered workflows to accelerate analysis, automate reporting, and improve decision-making.
Your work will go beyond models: you’ll generate actionable insights into user behavior, product funnels, and financial performance, helping drive both day-to-day decisions and long-term strategy.
If you’re excited about blending hands-on engineering, business analytics, and AI-driven workflows, and about seeing your work have direct impact on thousands of users, you’ll feel right at home here.
What you’ll do
-Build and maintain services that power decision engine, the core of our product -Design and develop statistical and machine learning models to support underwriting, fraud detection, and user engagement -Write production-ready Python code (FastAPI, Pandas, SQLAlchemy) to deploy models and pipelines into live systems -Develop and optimize ETL jobs and data pipelines to transform raw data into actionable insights -Write and maintain SQL queries to analyze user behavior, product performance, and financial metrics -Partner with product and business teams to analyze funnels, unit economics, and risk metrics, translating insights into strategic decisions -Design and run data-driven experiments (A/B tests, causal analysis) to validate product improvements and policy changes -Leverage LLMs and AI-powered tools to accelerate data exploration, automate reporting, and support SQL/analysis workflows -Prototype and integrate AI-driven workflows (agents, RAG pipelines, anomaly detection, automated monitoring) in data stack -Implement and maintain unit and integration tests to ensure model and pipeline reliability -Communicate findings and recommendations clearly across both technical and non-technical stakeholders
What we’re looking for
-4–6+ years of experience in a data science, data analyst, or ML engineering role -Strong Python programming skills (comfortable writing production-ready code, not just notebooks) -Experience with SQL and relational databases (Postgres, MySQL, or similar) -Hands-on experience with Python data libraries (Pandas, NumPy, SciPy, scikit-learn, etc.) -Background in applied statistics and machine learning (classification, regression, clustering, etc.) -Experience deploying models, pipelines, or analytics workflows into production environments -Familiarity with data-driven experimentation (A/B testing, causal inference, or uplift modeling) -Experience leveraging LLMs and AI-powered tools to improve analysis and reporting productivity -Strong problem-solving and communication skills, with the ability to collaborate in a remote-first environment -A responsible, detail-oriented mindset with a strong sense of ownership -Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or related field
Nice to have -Experience with ETL/data engineering frameworks (Airflow, dbt, or similar) -Familiarity with cloud platforms (AWS preferred: RDS, S3, ECS, Lambda) -Practical experience with advanced LLM applications, e.g., multi-agent workflows, automated feature engineering, anomaly detection agents -Familiarity with retrieval-augmented generation (RAG) and vector databases (e.g., Pinecone, Weaviate, FAISS) -Experience optimizing AI/data pipelines for latency, cost efficiency, and reliability -Experience with Docker and containerized workflows -Previous experience in fintech, risk modeling, or credit underwriting -Preference for candidates with startup experience and a diverse range of roles in their career