Staff Data Scientist, Technical Lead – Full-Stack, Production ML

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership of the complete delivery process for impactful data science projects, transitioning from vague business requests to systems ready for production.

• Design and uphold data pipelines, data models, and governance standards.

• Develop, assess, and refine machine learning models in a production environment.

• Lead the rigor of experimentation, monitoring, and management of the model lifecycle.

• Architect and deliver features integrated with large language models (LLMs) and agentic workflows, including prompt engineering, tool usage, and evaluation of outputs.

• Guide the architecture of cloud infrastructure for data science initiatives, focusing on performance, maintenance, and cost efficiency.

• Write high-quality Python and SQL for production, enforce code review standards, and maintain comprehensive documentation.

• Collaborate with engineering teams to integrate models and pipelines into the core product infrastructure.

• Convert ambiguous business challenges into clearly defined analytical and modeling tasks with established success metrics.

• Collaborate with product, engineering, and business stakeholders.

• Contribute to the roadmap for data and analytics.

• Communicate technical insights and influence decision-making based on data and model results.

• Lead a small team of data scientists while also acting as a player-coach.

• Set the technical vision and mentor the team, with the potential for direct management growth.

• Report directly to the Director of Data Science.

• Travel may be required up to 10% for training and conferences.

⛳️ Requirements

• A minimum of 6 years of experience in data science, data engineering, or a closely related technical field.

• At least 1 year of formal experience as a technical lead overseeing a team.

• Proficient in SQL and Python at an expert level; capable of writing production code rather than just analysis scripts.

• Comprehensive understanding of data infrastructure, including pipelines, warehousing, data modeling, and the behavior of source systems.

• Strong software engineering practices, including version control, code reviews, testing, and CI/CD pipeline experience for data or ML workloads.

• Ability to independently scope and deliver complex analytical projects based on vague inputs.

• Experience with cloud data platforms such as Snowflake, Azure, AWS, or GCP.

• Familiarity with infrastructure-as-code tools like Terraform or CloudFormation.

• Experience with containerization technologies such as Docker, preferably with Kubernetes or a managed container service.

• Background in making and justifying decisions regarding build-vs-buy or cost/latency trade-offs in a production ML or data system.

• Familiarity with ML platform tools like MLflow, feature stores, or model registries.

• A required Master's degree or Bachelor's degree in a technical or quantitative field.

• Preferred: proficiency in dbt, Airflow, Spark, or similar technologies.

• Preferred: demonstrated experience with LLMs, including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, or agent frameworks.

• Preferred: understanding of agentic AI architectures involving multi-step reasoning, tool utilization, memory, and orchestration.

• Preferred: experience in retail, fraud detection, or handling transaction-level data at scale.

🏝️ Benefits

• A variety of medical plan options.

• Coverage for dental and vision needs.

• Health savings and flexible spending accounts.

• Paid parental leave.

• Additional coverage for life's unexpected situations.

• Generous paid time off policy.

• 401(k) plan with immediate vesting and company matching.

• Short-term and long-term disability coverage.

• Complimentary access to health and wellness resources like Calm and Sworkit.

• Opportunities for learning and professional development.

• A bonus of 12–15% on top of the base salary.