Data Scientist Mid Level

Limited Information Available on Specific Role Search results from BuiltIn NYC do not include details on a "Data Scientist (Mid-Level)" role at a specific company with a Big Data & ML focus, nor any matching internship/graduate program for ages 18-25. The provided link points to a general job search page for remote data science roles in New York, NY, but no exact match appears. BuiltIn NYC itself operates as a job board platform focused on NYC tech roles in AI, fintech, SaaS, and big data, connecting seekers with employers like those in AI and ML sectors. Company Intelligence (NYC Tech Ecosystem via BuiltIn NYC)

• History, Size, Industry Position: BuiltIn NYC is a leading job platform for NYC's tech scene, which employs 549,200 tech workers (6% of workforce per 2024 CompTIA survey). Key industries include AI and fintech; $25.5B in 2024 VC funding (Pitchbook). Major employers: Capgemini, Bloomberg, IBM, Spotify. Notable investors: Greycroft, Thrive Capital, Tiger Global.
• Recent News/Growth: NYC tech hubs like AI Now Institute and Flatiron Institute drive ML innovation; universities (Columbia, NYU) feed talent pipelines.
• Culture/Work Environment: Listings emphasize high-growth startups (e.g., Harvey: "sharp, motivated team... thrive in ambiguity"; Tatari: AI/ML focus, rapid scaling). Remote/hybrid common in NYC listings.
• Values/Mission: Platforms highlight ownership, customer focus, excellence (e.g., "move fast, operate with intensity").
• Locations/Policies: Primarily NYC-focused with remote options; e.g., roles tagged "Remote New York, NY." Program Deep Dive (General for Mid-Level Data Roles on BuiltIn NYC) No student/recent grad program details found for this title. Mid-level data science roles typically suit experienced hires, not entry-level interns (contrast with Software Engineer Intern at Tatari).
• Structure/Timeline: Not specified; assume standard full-time hires, not timed programs.
• Skills: Big Data/ML implies Python, SQL, TensorFlow, scikit-learn; NYC AI focus suggests real-world ML deployment.
• Responsibilities/Learning: Build models, analyze data for business impact (inferred from ecosystem).
• Mentorship/Progression: High-growth firms offer ownership paths; e.g., Harvey emphasizes "personal/professional growth." Application Success Guide
• Requirements/Deadlines: Apply via BuiltIn NYC links; tailor resume to ML projects, GitHub portfolio. No deadlines listed—apply ASAP as postings are live.
• Process:

1. Submit via URL;
2. Recruiter screen;
3. Technical interviews (coding, ML case studies);
4. Behavioral fit.

• Interview Questions: Expect "Build a recommendation system," "Handle imbalanced datasets," "Explain gradient descent." Company-specific: Ambiguity handling (e.g., "How do you prioritize features in ML?").
• Assessments: Likely HackerRank-style coding, ML case studies on Kaggle-like problems.
• Standout Traits: Portfolio with deployed ML models; NYC AI knowledge (e.g., cite Flatiron Institute work). Insider Tips
• Interviews: They value ownership in ambiguity—use STAR method for examples; demo end-to-end ML projects.
• Skills Priority: Technical (ML frameworks, big data tools like Spark) over soft, but show collaboration (e.g., cross-team impact).
• Industry Knowledge: Reference NYC's $25.5B VC in AI/fintech; know local players like Two Sigma Ventures.
• Questions to Ask: "How does the team measure ML model success in production?" "What's the biggest data challenge you're solving now?" Shows interest in impact.
• Red Flags: Generic resumes; no ML portfolio; inability to discuss trade-offs (e.g., accuracy vs. speed). Practical Information
• Salary: Mid-level data scientists in NYC: $150K–$220K base (aligned with similar roles like Controller at $151K–$227K); equity common.
• Benefits: Health (medical/dental/vision), HSA, home-office stipend, commuter perks, equity for all.
• Start/Duration: Full-time, immediate starts; not program-based.
• Networking: Leverage BuiltIn NYC events, NYU/Columbia alumni; connect via LinkedIn with posters like Tatari (AI/ML ads). Actionable Next Steps for 18-25 Year-Olds

1. Build ML portfolio: Kaggle competitions, personal big data projects (e.g., NYC transit analysis).
2. Target entry-level alternatives on BuiltIn NYC (e.g., intern roles like Tatari's).
3. Network: Attend NYC AI meetups; reference ecosystem stats in apps.
4. Apply broadly—mid-level may flex for strong grads with internships. Search BuiltIn NYC directly for "data intern" or "ML graduate" filters.