Senior Data Scientist
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Job Description
Senior Data Scientist building PySpark, Python, and SQL workflows for KYROS, a remote actuarial consultancy focused on loyalty programs. Delivering predictive models and client-ready insights from structured transactional data.
Responsibilities:
- Help deliver a growing portfolio of client projects
- Translate clients’ business problems into full end-to-end data science workflows
- Work directly with clients to define data requirements
- Build data pipelines to ingest and transform raw loyalty program data into clean, structured datasets for analysis and modeling
- Design, build, and evaluate predictive models
- Make decisions around feature engineering, methodology, and trade-offs
- Synthesize analytical results into clear stories for non-technical clients
- Support client business decisions with data-driven insights
- Perform ad hoc analyses and address novel problems
- Build new solutions and design new analytical approaches
- Contribute to the evolution of the analytics platform
- Own the end-to-end data science workflow from data through insights
Requirements:
- At least 5 years of professional experience in a data science, data engineering, or analytically focused role
- Bachelor’s or master’s degree in math, economics, bioinformatics, statistics, engineering, computer science, actuarial science or other quantitative field
- Demonstrated hands-on experience using Python and/or PySpark to clean, transform, and manipulate large, complex, and messy datasets
- Strong Python and/or PySpark expertise
- Strong SQL expertise
- Ability to build and maintain production-quality analytical workflows
- Advanced Excel skills
- Ability to connect technical work to business goals
- Familiarity with predictive modeling techniques used in an actuarial, data science, or predictive analytics focused role
- Ability to clearly explain modeling approaches, assumptions, and results to non-technical audiences
- Strong communicator
- Growth mindset
- Ability to clearly explain modeling techniques, methodologies, assumptions, and results to both technical and non-technical audiences
- Actuarial background, direct experience building and analyzing P&C actuarial models end-to-end, or ACAS/FCAS qualifications are a plus
- Demonstrated experience working with big data frameworks (e.g., Hadoop, Spark)
- Experience with dashboarding/data visualization (e.g., Plotly Dash, Power BI)
Benefits:
- Retirement Plan (401k with employer match)
- Qualified Small Employer Health Reimbursement Arrangement (QSEHRA)
- Discretionary PTO
- Fully remote
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