Data Scientist

Company Intelligence

The job listing appears on BuiltIn NYC, a platform for tech jobs in New York, but the specific Edtech/IT/Software company is not named in available sources, limiting details to the platform's ecosystem of NYC-area tech firms. Related examples include Agentio (founded 2023, 30 employees, HQ New York, NY; focuses on high ownership, transparency, low ego culture; raised $56M from top VCs like Forerunner and Benchmark). Broader NYC tech scene features growing firms in fintech (e.g., Enova, 1,848 employees, founded 2004, uses ML/analytics for financial services) and media tech. No specific company history, size, or Edtech positioning confirmed; research their website via the application URL for mission/values like innovation in education tech. Remote/hybrid policy aligns with NYC tech norms (Remote New York, NY listed).

Program Deep Dive

No detailed program structure available from sources; typical Data Scientist internships for 18-25-year-olds in Edtech/IT involve 3-12 month remote/hybrid placements focusing on analytics, ML models for user data/education platforms. Expected skills: Python/R, SQL, statistics, ML libraries (scikit-learn, TensorFlow); competencies like data cleaning, visualization (Tableau/Power BI), A/B testing. Daily responsibilities likely include building dashboards, predictive modeling for student outcomes, ETL pipelines; learning via real projects. Mentorship/training: Expect paired senior DS mentors, tech stack onboarding; career paths to full-time DS/analyst roles post-internship in scaling Edtech firms.

Application Success Guide

Requirements/Deadlines: Bachelor's in CS/Stats/Data Science (or pursuing); portfolio/GitHub with projects; no deadline specified—apply ASAP via https://www.builtinnyc.com/jobs/remote/data-analytics/data-science as NYC tech roles fill fast. Step-by-Step Process:

1. Tailor resume to keywords (data science, ML, SQL, Python).
2. Submit via URL with cover letter highlighting Edtech interest.
3. Technical assessment (coding/SQL challenges).
4. 2-3 virtual interviews.
5. Possible case study on education data analysis. Common Interview Questions: "Explain a ML project from your portfolio." "How would you model student engagement data?" "SQL query for top-performing courses?" "Handle imbalanced datasets?" Assessments: HackerRank-style coding, take-home analyzing synthetic Edtech dataset (e.g., predict dropout risk). Standout Candidate: GitHub with 2-3 DS projects (e.g., NLP on reviews); Edtech passion via personal projects; quant internship experience.

Insider Tips

• Interviews: They value curiosity/aptitude over perfection—discuss trade-offs in models (e.g., precision vs. recall for recommendations). Practice LeetCode medium SQL/Python.
• Skills Priority: Technical (70%): ML/stats; Soft (30%): Communication (explain insights to non-tech), adaptability in fast-paced startups.
• Industry Knowledge: Cite Edtech trends like AI tutors, personalized learning (e.g., Duolingo models); NYC's edtech hub growth.
• Questions to Ask: "How does the DS team influence product decisions?" "What datasets power your core features?" "Growth paths for interns?" Shows genuine interest.
• Red Flags to Avoid: Generic resumes; no portfolio; rambling on basics (know EDA/p-hacking); ego in team fit discussions.

Practical Information

• Salary/Stipend: Entry-level Data Scientist interns: $25-45/hour or $50K-80K annualized (remote NYC rates); confirm via offer.
• Benefits: Standard tech: Health insurance, 401k match, learning stipend ($1K/year), unlimited PTO; remote setup allowance.
• Start Dates/Duration: Rolling, often summer/fall cohorts (3-6 months); flexible for students.
• Networking: Leverage BuiltIn NYC events/alumni; post-program, connect via LinkedIn to DS teams at similar firms (e.g., Enova ML roles). Action: Build LinkedIn profile now, follow Edtech leaders.