Senior Data Scientist

Job Specification: Data Scientist (Mid-Senior Level)
Role Requirements:
Technical Skills & Tools:

• Must-have:
  • Proficiency in statistical modeling and classical machine learning techniques (supervised learning, reinforcement learning basics)
  • Experience with experimental design, A/B testing, and analysis
  • Familiarity with CI/CD workflows and collaboration with software/data engineering teams
  • Programming experience in Python and SQL (coding proficiency secondary to analytical maturity)
  • Ability to evaluate, tune, and operationalize supervised learning models, including contextual bandits
• Nice-to-have:
  • Experience working with ML predictions in e-commerce or similar environments
  • Knowledge of fintech or telecom industries (transferable experience)

Experience & Background:

• Preferred:
  • Work experience with 5+ years in data science roles
  • Demonstrated ability to translate ambiguous business questions into modeling strategies independently
  • Prior experience supporting deployment and operationalization of models within software teams
• Not required:
  • PhD degree (experience prioritized); PhDs valued for independence and tenacity

Certifications & Education:

• Not mandated; relevant work experience valued over formal education

Soft Skills & Working Style:

• Highly autonomous, capable of handling unstructured tasks with minimal guidance after approximately 6 months
• Strong problem translation skills, with ability to communicate effectively with stakeholders
• Demonstrated capacity to operationalize data science solutions in collaboration with technical teams
• Proactive, independent thinker with problem-solving aptitude

Location & Work Setup:

• Preferred: Washington D.C. area; possible NYC location
• Work arrangement: 1 day/week on-site initially; potential for fully remote work later
• Employment type: Full-time, direct hire
• Salary range: $170,000

Additional Considerations:

• Candidates should have experience supporting practical, operational data science initiatives in a collaborative environment
• Emphasis on practical experience, independence, and ability to partner effectively with software and data teams; coding skills are essential but secondary to analytical and problem-solving skills