Principal Data Scientist

Job-:
Data Science & Analysis

Location:

Hartford, Connecticut, United States Workplace:
Hybrid Posted:

Job Role:

Data Science-Machine learning Engineer Experience (In Years): 6-9

Job Location:

Hartford, CT

Work Location:

United States Work Mode      :
Remote / Potential hire can work from Connecticut / Texas or from Pittsburgh Pay Range       :$90K-$120K /Yr Base + Annual Bonus The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position.

The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process

For more information on benefits and what we offer please visit us at Job Overview:

We are hiring a 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.

Qualifications Required qualifications

Bachelor's or Master's in Data Science, Statistics, Computer Science, Mathematics or a related quantitative field (or a bachelor's with equivalent experience).9+ 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 Tensor Flow.

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, Sage Maker, Azure ML, Vertex AI).

Experience with cloud data platforms such as Snowflake, Databricks, Big Query or Microsoft Fabric.

Familiarity with Spark or other distributed processing frameworks.

Domain experience in healthcare industry.

Publications, patents or open-source contributions.

Relevant cloud or ML certifications.

Responsibilities 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 GenAIApply 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…