Senior Data Scientist (Remote) - Drive ML Impact

Prog Leasing, LLC; Northern (KY); Hybrid; USD 120,000 - 190,000; Full time

Benefits offered by this job

Competitive compensation

Full health benefits

Company matched 401k

Paid time off + holidays

Employee stock purchase program

Job summary

Progressive Leasing is seeking a Senior Data Scientist to lead the development of decision systems and predictive models, working remotely across the continental US. You will blend analytics, data mining, and software engineering to deliver measurable business impact and guide strategic directions.

Applicants should have 4+ years of experience, a Master’s in a quantitative field, and strong R/Python/SQL skills. Experience mentoring others and deploying models is preferred.

Qualifications

• Master's degree in a quantitative/technical field.
• 4+ years of experience in data science or a related role.
• Proficiency in R and Python; strong SQL skills.
• Experience applying advanced models to real-world business problems and delivering impact.

Responsibilities

• Lead the design, build, and maintenance of decision systems and predictive models driving business impact.
• Explore internal and external data sources to improve model performance.
• Design and read in-market experiments to test new algorithms and processes.
• Prototype solutions for ambiguous problems using modern techniques.
• Mentor junior data scientists and contribute to code reviews and standards.

Skills

R

Python

SQL

Machine learning

Education

Master's degree

Tools

TensorFlow

scikit-learn

Git

AWS GC P

Job description

Progressive Leasing is seeking a Senior Data Scientist to lead the development of decision systems and predictive models, working remotely across the continental US. You will blend analytics, data mining, and software engineering to deliver measurable business impact and guide strategic directions.

Applicants should have 4+ years of experience, a Master’s in a quantitative field, and strong R/Python/SQL skills. Experience mentoring others and deploying models is preferred.