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About This Role
United States \- New York
Digital, Product Management and Development
Global Wealth Management
Job Reference \#
338661BR
City
New York
Job Type
Full Time
Your role
Are you an analytical thinker who enjoys leveraging data and advanced analytics to generate actionable insights that enhance client experience and drive business growth?
We’re looking for a data scientist to:
- lead and mentor a team of 10–15 highly capable data scientists focused on building impactful AI applications
- identify and prioritize opportunities to apply AI for workflow optimization and revenue generation through strong stakeholder engagement
- drive end\-to\-end development and deployment of AI/ML models, applying best practices in software engineering and reproducible research
- design, build, and maintain LLM evaluation frameworks to monitor model performance, reliability, and consistency throughout the development lifecycle
- partner closely with product, business, and technology teams to deliver robust, production\-grade AI solutions
- communicate complex technical concepts clearly and effectively to both technical and non\-technical stakeholders
Your team
You will be part of Data Analytics \& Foundational Platforms (DAFP) within Wealth Management Americas (WMA), focusing on developing statistical and machine learning models that enable better decision\-making across the business.
Our global Data Science team operates across the US, Poland, and China, and plays a central role in supporting data science initiatives across multiple business areas.
We believe diversity strengthens our organization and are committed to fostering an inclusive environment that drives better outcomes for our people and our clients.
Your expertise
- master’s or PhD in Machine Learning, Computer Science, Computational Linguistics, or a related technical field
- ideally 7–10 years of experience leading teams and delivering advanced analytics or AI\-driven products
- strong knowledge of statistical tools (e.g., R, SAS), programming languages (Python), data visualization tools, and advanced analytical methods including machine learning and AI
- excellent active listening and communication skills, with the ability to build partnerships across all levels of the organization
- proven leadership experience in building, mentoring, and scaling high\-performing data science teams
- strong problem\-solving mindset with the ability to translate business challenges into scalable analytical solutions
- ability to operate effectively in a fast\-paced, cross\-functional environment
- proficiency in Python and key frameworks/libraries such as PyTorch, transformers, LangChain, OpenAI, and spaCy
- experience working with LLM APIs (e.g., Azure OpenAI, Anthropic)
- strong understanding of version control and software engineering best practices
- familiarity with cloud platforms (Azure, AWS, GCP, Databricks)
- experience working with SQL databases
- excellent written and verbal communication skills
- experience building scalable information retrieval and enterprise search solutions using both classical (keyword\-based) and modern (semantic / embedding\-based) techniques; exposure to RAG and agent\-based systems is highly desirable
- experience designing automated evaluation frameworks for LLM applications
- strong familiarity with the Hugging Face ecosystem (transformers, datasets, PEFT, accelerate, tokenizers, evaluate)
- hands\-on experience with LLM training and fine\-tuning techniques such as PEFT, LoRA, quantization, RLHF, and DPO
- experience optimizing workflows that rely on rate\-limited APIs
- nice to have: Publications in NLP or related fields
- nice to have: Experience in the financial services industry
- nice to have: Contributions to open\-source projects (e.g., GitHub)
About us
UBS is a leading and truly global wealth manager and the leading universal bank in Switzerland. We also provide diversified asset management solutions and focused investment banking capabilities. Headquartered in Zurich, Switzerland, UBS is present in more than 50 markets around the globe.
We know that great work is never done alone. That’s why we place collaboration at the heart of everything we do. Because together, we’re more than ourselves. Want to find out more? Visit ubs.com/careers.
Salary information
The indicative gross base salary range as a full\-time equivalent role:
- United States \- New York \- New York min USD 140000 \- max USD 180000 /annum
The expected salary for this role will be determined by relevant factors which may include but are not limited to, role\-required experience, qualifications, education, location and skill level. UBS offers a range of competitive benefits and for further information, please visit ubs.com/employee\-benefits. We may, at our sole discretion, provide additional variable compensation or awards.
Join us
At UBS, we know that it's our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. We’re dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey. And we use artificial intelligence (AI) to work smarter and more efficiently. We also recognize that great work is never done alone. That’s why collaboration is at the heart of everything we do. Because together, we’re more than ourselves.
We’re committed to disability inclusion and if you need reasonable accommodation/adjustments throughout our recruitment process, you can always contact us.
Disclaimer / Policy statements
UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.
Salary Context
This $140K-$180K range is below the median for Data Scientist roles in our dataset (median: $162K across 211 roles with salary data).
View full Data Scientist salary data →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 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At UBS, 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 $200,000 based on 697 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 20% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
UBS AI Hiring
UBS has 4 open AI roles right now. They're hiring across AI Product Manager, Data Scientist. Based in New York, NY, US. Compensation range: $93K - $250K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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