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About This Role
WE'RE HIRING IMMEDIATELY
Data Scientist / Data Engineer
*NYC Property\-Tax Appeal Practice — In\-Office — Immediate Start*
We need to fill this position right away. We are looking for someone to manage our office Google Sheets, lead our AI projects, and own day\-to\-day data management for a New York City property\-tax appeal practice. If you're ready to start immediately and have the background below, we want to hear from you today.
Role Summary
This role is the firm's go\-to person for office data and AI\-driven tools. You'll manage our Google Sheets\-based tracking systems, lead our applied\-AI initiatives (including AI\-assisted drafting workflows), and keep our data clean, organized, and reliable across the practice. You'll also help maintain our PostgreSQL database and support light automation of recurring office tasks. You'll work directly with attorneys and analysts, turning everyday business needs into simple, dependable tools. Deeper software engineering work (application development, cloud infrastructure, legacy system migration) is part of the role but is not the main day\-to\-day focus — strong candidates without that background are still encouraged to apply.
Key Responsibilities
- Google Sheets and office data management — Own and maintain the firm's Google Sheets\-based tracking and reporting tools; keep data accurate, organized, and properly access\-controlled.
- AI project ownership — Lead our applied\-AI initiatives, including AI\-assisted document drafting; manage prompting, review, and quality control of AI\-generated output.
- Data management — Organize, clean, and maintain data across our systems; run recurring data refreshes and produce ad hoc reports and extracts for attorneys and operations.
- Light automation — Build simple scripts or tools to cut down on repetitive manual work (data entry, document generation, email tasks, and similar).
- Database support — Help maintain our PostgreSQL database: routine updates, data\-quality checks, and basic troubleshooting.
- Stakeholder collaboration — Work with attorneys, analysts, and operations staff to understand what they need and deliver practical, easy\-to\-use solutions.
Required Qualifications
- Experience managing Google Sheets (or similar spreadsheet systems) for a team or office, including data accuracy and access control.
- Hands\-on experience using AI tools (e.g., ChatGPT, Claude, or similar) for real business or drafting tasks.
- Strong general data management skills — organizing, cleaning, and maintaining data so it stays reliable over time.
- Basic to intermediate Python and/or SQL skills for automating routine tasks.
- Comfort working in a Windows\-based office environment.
- Ability to work independently, handle sensitive data responsibly, and communicate clearly with non\-technical attorneys and staff.
- Strong attention to detail in deadline\-sensitive work.
- Available to start immediately.
Preferred Qualifications
- Experience with PostgreSQL or a comparable relational database, including schema design, backups, and recovery.
- Experience building or maintaining web applications, internal tools, or APIs (e.g., Django or similar).
- Experience migrating legacy systems (e.g., FoxPro/dBase) to a modern database.
- Experience with cloud platforms (Azure, AWS, or similar), Docker, or CI/CD.
- Experience with more advanced AI/LLM work: prompt engineering, RAG, fine\-tuning, or model evaluation.
- Experience with OCR, PDF processing, or Microsoft Office document automation.
- Experience with web scraping or working with NYC government or other public data sources.
- Background in data analysis or statistics (pandas, R, or similar).
- Exposure to property tax, real estate, legal, regulatory, or government workflows.
To Apply
This is an immediate hire — we are reviewing applications on a rolling basis and looking to fill this role as soon as possible. Interested candidates should submit a resume as soon as possible.
Pay: $65,000\.00 per year
Benefits:
- Dental insurance
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Retirement plan
- Vision insurance
Work Location: In person
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Berger, Goldberg, Friedman, & Perlman, P.C., this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Berger, Goldberg, Friedman, & Perlman, P.C. AI Hiring
Berger, Goldberg, Friedman, & Perlman, P.C. has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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