Principal Data Scientist

EXL•Hartford, CT

1d•$90,000 - $120,000•Remote

About The Position

The Principal Data Scientist will serve as the most senior technical voice in data science, translating business problems into practical machine learning and generative AI solutions and enabling the broader team to build, deploy, and maintain them. The role covers end-to-end solution design and delivery, technical guidance and mentorship, collaboration across onshore and offshore teams, advanced analytics and GenAI, and governance and stakeholder partnership. The position is remote in the United States, with potential hires able to work from Connecticut, Texas, or Pittsburgh.

Requirements

Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field; alternatively, a bachelor's degree with equivalent experience.
At least 9 years of experience in data science or applied machine learning, with 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.
Ability to communicate clearly 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 with 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, from data requirements and modeling approach through deployment and monitoring.
Build reference implementations, reusable components, and templates for the team to extend.
Make build, buy, and reuse decisions while balancing accuracy against cost, speed, and maintainability.
Write design documents, runbooks, and code standards to enable others to build and support solutions.
Review code, models, and experiments, setting quality and reproducibility standards.
Mentor data scientists and ML engineers and run knowledge-sharing sessions.
Hand over solutions and remain available to unblock the team through delivery.
Collaborate with onshore and offshore teams using shared standards, tools, and practices.
Run design reviews and planning sessions across time zones and document decisions clearly.
Maintain context and communication 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, such as summarization, document extraction, and natural-language data access, and bring proven use cases to 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.