Data Scientist Mid Level

This mid-level Data Scientist role listed on BuiltIn NYC is not an internship or graduate program for students/recent graduates (ages 18-25); it targets experienced professionals with substantial prior work in big data and ML. BuiltIn NYC serves as a job board aggregating NYC tech opportunities in AI, fintech, and big data, rather than the employer itself—lacking a dedicated early-career program based on available data. For young professionals, prioritize entry-level data roles on the platform (e.g., analyst positions) to build toward mid-level; here's structured intel drawn from platform patterns and NYC tech context.

Company Intelligence

BuiltIn NYC is a leading NYC tech job platform connecting talent with employers in AI, fintech, SaaS, and big data—key growth sectors with $25.5B in 2024 VC funding.

• History/size/position: Focuses on NYC's 549,200 tech workers (6% of workforce); top employers include Capgemini, Bloomberg, IBM, Spotify; strong in high-growth startups backed by Greycroft, Thrive Capital.
• Recent news/growth: Platform highlights scaling companies (e.g., 3x growth at Faraday, $100M run-rate at Vibe); NYC tech ecosystem expanding via AI research at Columbia/NYU.
• Culture/environment: Emphasizes high-output, low-ego teams thriving in ambiguity, ownership, and customer focus—common in listed firms like Harvey (1000+ customers).
• Values/mission: Build generational companies with urgency, excellence, and real impact; remote/hybrid common for NYC roles.
• Locations/policies: NYC-centric (Chinatown hubs noted); many remote like this role; some global (e.g., UK offices for Celonis).

Program Deep Dive

No structured internship/grad program identified for this listing—it's a mid-level full-time hire expecting 3-5+ years in data science/ML, not entry-level training.

• Structure/timeline: Standard employment, likely 6-12 month ramp-up; remote from day one.
• Skills sought: Advanced big data/ML (e.g., Python, SQL, TensorFlow, Spark); stats, model deployment; NYC AI focus implies cloud (AWS/GCP).
• Responsibilities/learning: Analyze datasets, build ML models, optimize pipelines; exposure to production-scale big data in fintech/AI firms.
• Mentorship/training: Varies by employer (e.g., Harvey's sharp teams); expect peer collaboration over formal programs.
• Progression: Path to senior data scientist/lead; high-growth firms offer rapid promotion (e.g., 3x YoY scaling). Actionable pivot for 18-25s: Scan BuiltIn NYC for "junior data analyst" or "data intern" in big data/ML—many list mentorship and skill-building.

Application Success Guide

Application via BuiltIn NYC link; rolling basis, apply ASAP as tech roles fill fast.

• Requirements/deadlines: Resume, portfolio (GitHub with ML projects), cover letter tailoring big data experience; no degree specified but STEM preferred.
• Process:

1. Submit via URL with LinkedIn integration.
2. Recruiter screen (30-min call).
3. Technical assessment (coding/SQL/ML case).
4. Interviews (2-4 rounds: behavioral + live coding).
5. Offer. (Pattern from similar listings.)

• Interview questions: "Design an ML pipeline for fraud detection" (fintech nod); "Explain gradient descent"; "Handle imbalanced big data sets?"; behavioral: "Describe ambiguous project ownership."
• Assessments: Take-home Kaggle-style dataset analysis; live SQL/ML on HackerRank/CoderPad.
• Standout traits: GitHub with deployed models; NYC tech passion (mention AI Now Institute); quantify impact (e.g., "Optimized model 20%").

Insider Tips

• Interviews: They value ownership in ambiguity—use STAR stories showing initiative; demo ML end-to-end.
• Skills balance: Technical heavy (70%: ML frameworks, big data tools) + soft (30%: urgency, collaboration); code clean, explain trade-offs.
• Industry knowledge: Cite NYC's AI/fintech boom ($25.5B VC), tools like Databricks for big data.
• Questions to ask: "How does the team iterate ML models in production?"; "What's the biggest big data challenge now?"—shows depth.
• Red flags: Generic resumes; weak portfolio; no production ML experience; complaining about ambiguity.

Practical Information

• Salary/stipend: Mid-level NYC remote: $130K-$170K base + equity/bonus (per 2024 benchmarks; entry-level half that).
• Benefits: Standard tech (health, 401k, unlimited PTO); remote setup stipend common.
• Start/duration: Flexible, ongoing hires; indefinite full-time.
• Networking/alumni: Leverage BuiltIn NYC events, LinkedIn (follow NYC AI groups); alumni in scaling startups like Harvey/Faraday. For 18-25 success: Build portfolio now (Kaggle competitions, personal ML projects); apply to 10+ junior roles weekly on BuiltIn NYC; network via NYU/Columbia events. This mid-level is a 2-3 year target post-entry roles.