Principal Data Scientist
About The Position
The Principal Data Scientist is the most senior technical voice in data science, translating business problems into practical machine learning and generative AI solutions and enabling the wider team to build, deploy, and maintain them. The role designs end-to-end solutions, including data requirements, modeling approaches, deployment, and monitoring, and develops reusable implementations and standards for team use. It also mentors data scientists and ML engineers, supports collaboration between onshore and offshore teams, applies advanced analytics and GenAI, and partners with leaders and technical teams on governance, responsible AI, privacy, and security. The position is remote in the United States, with potential hiring from Connecticut, Texas, or Pittsburgh. The posted base salary range is $90,000–$120,000 per year, plus an annual bonus. Final compensation depends on factors including skills, experience, pay equity, work location, market conditions, and role scope; compensation in certain higher-cost zones may exceed the posted range.
Requirements
- Bachelor's or master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field, or a bachelor's degree with equivalent experience.
- At least 9 years of experience in data science or applied machine learning, including a record of solutions running in production.
- Strong Python and SQL skills.
- Experience with common libraries such as scikit-learn, pandas, PyTorch, or TensorFlow.
- Deep knowledge of statistics, experimental design, and core machine learning techniques, including supervised and unsupervised learning, time series, and NLP.
- Demonstrated experience designing solutions that other teams implemented and maintained, with documented patterns and handoff.
- Experience leading or mentoring data scientists and engineers, formally or as a technical lead.
- Experience working with distributed teams across time zones.
- Clear communication skills with technical teams and business executives.
Nice To Haves
- Hands-on experience with LLMs, including prompt design, retrieval-augmented generation (RAG), fine-tuning, and output-quality evaluation.
- MLOps experience, including model versioning, CI/CD for ML, monitoring, and retraining; examples include MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.
- Experience with cloud data platforms such as Snowflake, Databricks, BigQuery, or Microsoft Fabric.
- Familiarity with Spark or other distributed processing frameworks.
- Healthcare industry domain experience.
- Publications, patents, or open-source contributions.
- Relevant cloud or ML certifications.
Responsibilities
- Translate ambiguous business problems into well-scoped data science solutions with clear success metrics.
- Design end-to-end solutions covering data requirements, modeling approach, deployment, and monitoring.
- Build reference implementations, reusable components, and templates that the team can extend.
- Make build, buy, and reuse decisions, balancing accuracy against cost, speed, and maintainability.
- Write design documents, runbooks, and code standards so others can build and support solutions.
- Review code, models, and experiments, and establish standards for quality and reproducibility.
- Mentor data scientists and ML engineers and run knowledge-sharing sessions.
- Hand over solutions cleanly and remain available to unblock the team during delivery.
- Collaborate with onshore and offshore teams using shared standards, tooling, and ways of working.
- Run design reviews and planning sessions across time zones, documenting decisions clearly.
- Maintain context between business stakeholders and offshore delivery teams.
- Apply statistical modeling, machine learning, and forecasting to prediction, optimization, and segmentation problems.
- Identify and prototype GenAI use cases and bring proven applications into production.
- Define evaluation methods for models and LLM outputs, including accuracy, bias, drift, and cost.
- Advise senior leaders in plain language about what data science can and cannot deliver.
- Work with data engineering and platform teams on pipelines, MLOps, and model monitoring.
- Uphold responsible-AI, privacy, and security standards across solutions.
Benefits
- Annual bonus
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What This Job Offers
Job Type
Full-time
Career Level
Principal
