Principal Data Scientist

Robots and Pencils

About The Position

We’re looking for a Principal Data Scientist to join a multi-disciplinary team focused on designing and implementing scalable and reliable approaches to support or automate decision making throughout the business. This role is ideal for an experienced data scientist who can apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. In this role, you will acquire data by building the necessary SQL / ETL queries and import processes through various company specific interfaces for accessing S3, RedShift, and Spark storage systems. You’ll build relationships with stakeholders and counterparts, analyze data for trends and input validity, and implement models that comply with evaluations of computational demands, accuracy, and reliability.

Requirements

10+ years of data scientist experience with a proven track record of solving complex business problems through data science
Bachelor’s degree in Statistics, Applied Math, Operations Research, Economics, or a related quantitative field
Competency in data querying languages (e.g. SQL) and scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.)
Experience with statistical models (e.g., logistic regression, supervised learning approaches) and a solid foundation in machine learning methods
Excellent communications skills with non-technical executive audiences, with the ability to translate complex models and findings into clear, actionable insights
1+ year of hands-on experience with AI/ML technologies and modern machine learning frameworks
Demonstrated leadership and technical mentoring experience across a team or organization
Strong stakeholder communication skills with the ability to translate technical depth across audiences
Demonstrable, day-to-day usage and expert knowledge of AI-forward coding tools such as Claude and Cursor
Excellent problem-solving skills and the ability to navigate highly ambiguous technical and business challenges with sound judgment

Nice To Haves

Experience with handling and modeling data in the healthcare industry is a plus
AWS certifications, like Certified Data Engineer – Associate, strongly preferred
Experience with advanced machine learning frameworks and cloud-based data science platforms is a plus

Responsibilities

Design and implement scalable and reliable approaches to support or automate decision making throughout the business
Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear
Acquire data by building the necessary SQL / ETL queries and import processes through various company specific interfaces for accessing S3, RedShift, and Spark storage systems
Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies
Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks
Validate models against alternative approaches, expected and observed outcomes, and other business defined key performance indicators
Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production
Implement and deploy state of the art machine learning algorithms under Gen AI, build prototypes, troubleshoot customer issues, and explore new solutions
Interact closely with customers and with the academic community to drive innovation and deliver tailored data science solutions
Build relationships with stakeholders and counterparts to understand business needs and translate them into data science solutions
Collaborate closely with engineering, analytics, AI, and product teams to align data science models and insights with broader business goals
Communicate findings and model results clearly to non-technical executive audiences, ensuring insights are actionable and understood
Establish data science best practices and modeling standards that lift the quality and consistency of analytical work across the team
Mentor junior and mid-level data scientists, helping them grow their craft, confidence, and impact
Bring an AI-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to deliver higher-quality work at pace