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About This Role
We are seeking a Data Scientist II to join NYPA’s Data Analytics team. As NYPA continues to expand its investments in data science, Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI), and emerging technologies, this role will play a key part in developing advanced analytics solutions and enabling enterprise\-wide adoption of data\-driven decision making.
In this hands\-on data science position, you will design, build, and operationalize AI/ML models using both structured and unstructured datasets. You will collaborate closely with senior data scientists, and cross functional partners, including AI Fusion teams, to translate business objectives into practical, production\-ready analytical solutions. You will also help keep the organization informed on the evolving capabilities of AI/ML and GenAI, contributing to NYPA’s broader AI strategy and community of practice.
What You’ll Do
- Build, enhance, and deploy AI/ML solutions that address business needs using structured and unstructured data
- Translate business and technical requirements into scalable analytical models and insights
- Evaluate and test emerging AI/ML/GenAI technologies and keep the organization updated on advancements
- Collaborate with senior data scientists, data engineers, application developers, and cross functional partners, including AI Fusion teams
- Support NYPA’s Data Analytics and AI Community of Practice and contribute to enterprise AI strategy
- Work with modern cloud and distributed data tools to deliver reliable, high quality analytical outputs
The successful candidate will demonstrate hands on experience in advanced data analytics and AI/ML/GenAI techniques using tools such as Python, PySpark, Azure Foundry, and Databricks. They will also demonstrate strong communication and collaboration skills and bring a curiosity for the utility industry and emerging technology developments.
This is an excellent opportunity for a data scientist seeking growth, gaining meaningful experience in utility company while leveraging AI/ modern data analytic techniques, and the chance to contribute directly to NYPA’s digital transformation.
Responsibilities
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- Able to develop analytical model using various statistical methods to address business needs
- Works with greater independence, however, projects are still assigned by Manager/Director of Data Analytics.
- Works under moderate supervision, receiving advice and guidance from Lead Data Scientists.
- Work collaboratively with business partner to understand business analytical needs and propose solutions
- Applies standard practices and techniques to each situation, analyzes data, recognizes discrepancies in results, and follows operations through a series of detailed steps.
- Must meet deadlines set by senior level or above.
Knowledge, Skills and Abilities
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- Experience in programming languages such as Python, R, SQL, and experience working with data analysis libraries and frameworks (e.g., Pandas, NumPy, SciPy, scikit\-learn).
- Written communications skills must be at a level that can document facts (e.g. data, bug, issue, etc).
- Summarize analysis findings, write routing internal memos, compile weekly and monthly reports, report status of open issues or requests, as well as patch or maintenance notifications.
- Undertake programming, data analysis, and testing tasks, working within a team.
- General IT knowledge, and a basic understanding of enterprise systems. Perspective is limited to a specific system with awareness of NYPA IT operations and development procedures.
- General understanding of project management methodology and project work plans.
Education, Experience and Certifications
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- Bachelor’s Degree in data science, computer science, statistics, mathematics, or a related field.
- Minimum 2 years of related experience
Physical Requirements
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Overnight travel to the various NYPA locations within New York State.
The New York Power Authority is committed to providing fair, competitive, and market\-informed compensation. The estimated salary range for this position is: $102,000 \- $140, 800\. The salary offered will be determined based on the successful candidates’ relevant experience, knowledge, skills, and abilities.
The New York Power Authority and Canal Corporation believes that diversity, equity, and inclusion drive our success, and we encourage women, people of color, LGBTQIA\+ individuals, people with disabilities, members of ethnic minorities, foreign\-born residents and veterans to apply. As an equal opportunity employer, NYPA/Canals is committed to building inclusive, innovative work environments with employees who reflect communities across New York and enthusiastically serve them. We proudly celebrate diversity and do not discriminate based on race/color, creed/religion, national origin, citizenship or immigration status, age, disability, military status, gender/sex, sexual orientation, gender identity/expression, pregnancy and related conditions, familial/marital status, domestic violence victim status, predisposing genetic characteristics, arrest/criminal conviction record or any other category protected by law.
NYPA/Canals will also provide reasonable accommodations during the hiring process related to candidates’ disabilities, pregnancy\-related conditions, religious observances/practices and/or domestic violence concerns. To request an accommodation, please email [email protected].
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At New York Power Authority, 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 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
New York Power Authority AI Hiring
New York Power Authority has 1 open AI role right now. They're hiring across Data Scientist. Based in White Plains, NY, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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