Principal Data Scientist

Role summary

We are hiring a US based Principal Data Scientist to be our most senior technical voice in data science. You will turn business problems into practical machine learning and GenAI solutions, then make sure the wider team can build, deploy and maintain them. The role works across an onshore and offshore team, so clear design, documentation and mentoring matter as much as modeling skill.

Key responsibilities

Solution design and delivery

• 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 to deployment and monitoring.
• Build reference implementations, reusable components and templates the team can extend.
• Make build, buy and reuse decisions, and balance accuracy against cost, speed and maintainability.

Enabling the team to implement

• Write clear design documents, runbooks and code standards so solutions can be built and supported by others.
• Review code, models and experiments; set the bar for quality and reproducibility.
• Mentor data scientists and ML engineers, and run knowledge-sharing sessions.
• Hand over solutions cleanly, then stay available to unblock the team through delivery.

Onshore/offshore collaboration

• Work with onshore and offshore teams as one unit, with shared standards, tooling and ways of working.
• Run design reviews and planning sessions that suit multiple time zones, with clear written decisions.
• Keep context flowing between business stakeholders and offshore delivery teams.

Advanced analytics, ML and GenAI

• Apply statistical modeling, machine learning and forecasting to problems such as prediction, optimization and segmentation.
• Identify and prototype GenAI use cases (for example summarization, document extraction, natural-language data access) and take proven ones to production.
• Define evaluation methods for models and LLM output, including accuracy, bias, drift and cost.

Governance and stakeholder partnership

• Advise senior leaders on what data science can and cannot deliver, in plain language.
• Work with data engineering and platform teams on pipelines, MLOps and model monitoring.
• Uphold responsible-AI, privacy and security standards across all solutions.

Required qualifications

• Master's or Bachelor's in Data Science, Statistics, Computer Science, Mathematics or a related quantitative field (or a bachelor's with equivalent experience).
• 6+ years in data science or applied machine learning, with a record of solutions running in production.
• Strong Python and SQL; experience with common libraries such as scikit-learn, pandas, PyTorch or TensorFlow.
• Deep knowledge of statistics, experimental design and core ML techniques (supervised, unsupervised, time series, NLP).
• Proven 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 with both technical teams and business executives.

Preferred qualifications

• Hands-on experience with LLMs: prompt design, retrieval-augmented generation (RAG), fine-tuning and evaluating output quality.
• MLOps experience: model versioning, CI/CD for ML, monitoring and retraining (for example MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI).
• Experience with cloud data platforms such as Snowflake, Databricks, BigQuery or Microsoft Fabric.
• Familiarity with Spark or other distributed processing frameworks.
• Domain experience in our industry: [industry].
• Publications, patents or open-source contributions.
• Relevant cloud or ML certifications.

Skills and competencies

• Technical depth:

sound modeling judgment and the ability to pick the simplest approach that works.

• Pragmatism:

designs solutions the team can actually build, run and support.

• Technical leadership:

sets standards, reviews work and raises the skill of those around them.

• Communication:

writes clearly, documents decisions and explains models to non technical audiences.

• Cross-team collaboration:

builds trust across onshore and offshore teams and time zones.

• Business focus:

ties every model to a measurable outcome.

• Responsible AI:

weighs bias, privacy, security and explainability from the start.