Interested in this Data Scientist role at Great Gray Trust Company?
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About This Role
Why Great Gray?
At Great Gray Group, we strive to set the bar for the retirement services industry. Our goal is to deliver advanced retirement solutions that combine our core fiduciary services with robust investment options, innovative technology, and dedicated client service. We focus on making choices clearer, transitions smoother, and the client experience more delightful. Complacency isn't in our vocabulary. Every day, we look for opportunities to better serve our clients, be an excellent business partner, and earn the trust of those who rely on us. The Role
Great Gray is looking to add a Data Scientist onto our Data Science \& AI Team.The data scientist we seek comeswith strong experience in Python and its data science ecosystem (pandas, NumPy, PyDantic, FastAPI) and hands\-on experience building and deploying AI/ML solutions. You will be working collaboratively within an agile team, contributing to architectural decisions, and championing best practices in software development.
If you are passionate about applying data science and AI to solve real business problems, thrive in a collaborative environment, and care deeply about the quality and impact of your work you will do well here. Our team members are expected to take full ownership of our products as well as embrace efficiency and continuous improvement. We are not looking for a few rock stars, but rather very strong contributors who bring curiosity, rigor, and a collaborative spirit to every problem they tackle.
This is an exciting opportunity to grow alongside Great Gray as we expand our data science and AI capabilities. Your ability to develop models, surface insights, and collaborate across teams will directly shape how we serve the retirement market.
Location
This position will work remote from the United States.
Please note that remote employment in this role is restricted to candidates residing in states where Great Gray is currently registered as an employer. These states are limited to: CA, CO, CT, DC, DE, FL, GA, IL, IN, MA, MD, MI, MN, NC, NH, NJ, NV, NY, OH, PA, RI, SC, TN, TX and VA.
Visa sponsorship or transfer of an existing visa is not available for this position. Applicants must be authorized to work directly for any employer in the United States without visa sponsorship or transfer.
Responsibilities
- Build, train, and deploy machine learning models and data pipelines using Python, pandas, NumPy, and scikit\-learn, ensuring scalability and reliability in production.
- Design, implement, and continuously improve LLM\-powered applications and Retrieval\-Augmented Generation (RAG) pipelines, including prompt engineering, embedding strategies, vector store integration, and evaluation frameworks.
- Build and iterate on agentic AI systems that leverage tool use, multi\-step reasoning, and orchestration frameworks to automate complex workflows and decision\-making processes.
- Conduct exploratory data analysis (EDA) to uncover trends, patterns, and anomalies that inform business decisions and product strategy.
- Design and build data visualizations and dashboards that communicate key metrics and insights to both technical and non\-technical stakeholders.
- Diagnose and resolve complex data and model issues, minimizing drift and continuously improving pipeline efficiency and model accuracy.
- Drive technical excellence through rigorous model evaluation, identifying opportunities for improvement, and enforcing best practices in code quality, experiment tracking, and reproducibility.
- Contribute to innovation by exploring emerging AI/ML technologies, LLM integrations (Azure AI Foundry, AWS Bedrock), and agentic tooling (Cursor, Claude) to keep our solutions at the cutting edge.
- Collaborate closely with cross\-functional teams to translate business requirements into data\-driven insights, models, and high\-quality analytical solutions.
- A strong analytical mindset and the ability to translate complex data findings into clear business recommendations.
- Strong collaboration skills, fostering an environment of shared learning, experimentation, and innovation.
- A passion for rigorous data science practices, reproducible research, and driving excellence in model development and deployment.
- The ability to balance multiple modeling and research priorities while maintaining high\-quality, production\-ready outcomes.
- Complete other related duties as assigned
RequiredQualifications \& Experience
- Exhibits our Great Gray attributes: Growth Mindset, Disciplined Curiosity, Grit, Results Ownership, Collaboration.
- 3\+ years of experience as a Data Scientist or ML Engineer, with a track record of delivering production\-grade models.
- Proficiency in Python and the core data science stack: pandas, NumPy, scikit\-learn, and FastAPI.
- Strong proficiency in SQL and experience querying large datasets in cloud environments (Azure, AWS).
- Experience building and deploying ML models and APIs, including familiarity with model serving and monitoring.
Preferred Qualifications \& Experience
- Experience with large language models and AI platforms such as Azure AI Foundry or AWS Bedrock.
- Experience with AI\-assisted development workflows using tools like Cursor and Claude.
- A growth mindset with an eagerness to learn new technologies and adapt to evolving challenges.
- Comfortable navigating ambiguity.
- Entrepreneurial mindset to bring best practice ideas to the team.
- Hands\-on experience with Azure and AWS services, including cloud\-based ML workflows and managed AI services.
Tech Stack
Our current tech stack is a blend of existing and next\-gen tools/languages, and like our company itself, our software is always evolving.
- Frontend: Angular, React
- Backend: C\# .NET
- Data Layer: MSSQL, Python, Postgres, Databricks
- Infrastructure: Azure cloud
- Tooling: Cypress, Docker, GitHub, Cursor, SonarQube
Base Pay Range\*
$90,000 \- $120,000
- This base pay range is subject to change and may be modified in the future.
The pay range displayed above is the base pay compensation range that Great Gray expects to pay for this position at the time of this posting. Individual compensation within this range, or that may warrant a provision for pay beyond this range, depends on multiple factors, including, but not limited to, candidate’s prior education and relevant work experience and training as well as position location and local market demands. Our pay\-for\-performance culture also includes participation in an annual incentive bonus plan for this position which is not included in the ‘Base Pay Range’ noted above.
Benefits
We have a comprehensive and competitive benefits package at Great Gray. Some of the highlights are:
- Be an integral part of a high\-growth organization!
- Competitive compensation package
- Group medical, dental and vision insurance
- Employer\-paid life and disability insurance
- Annual well\-being stipend
- Eligible employees may also contribute to a 401(k) plan with an advantageous employer contribution model, upholding our mission to support our employees in retirement
Company Background
Great Gray is the leading independent provider of trustee and administrative services to Collective Investment Trusts (“CITs”), with over $370 billion in CIT assets under management, across more than 1,020 funds. We proudly work with more than 80 subadvisors, including leading firms such as AllianceBernstein, American Funds, BlackRock, Franklin Templeton, MetLife, Neuberger Berman, PGIM, PIMCO and Raymond James.
CITs are more than just an investment vehicle. They represent a forward\-thinking approach to retirement planning. These tax\-exempt, pooled investment vehicles are offered to employer\-sponsored retirement plans, like 401(k)s. CITs are comparable to mutual funds, but, because they are tailored for the institutional retirement market, they can offer distinct advantages, including efficient administration and cost\-effectiveness. CITs have a history dating back over 90 years; but they have gained favor over the past decade, driven by innovations, and Great Gray has been at the forefront.
Great Gray has consistently delivered year\-over\-year growth at an above market rate and is investing in the continued development of its core CIT business as well as complementary administrative services and technology solutions for the retirement market.
Madison Dearborn Partners (“MDP”) purchased Great Gray from Wilmington Trust in April 2023\. As a result, Great Gray is an independent company owned by funds affiliated with MDP.
Investor Background
MDP is a leading private equity investment firm based in Chicago. Since MDP's formation in 1992, the firm has raised aggregate capital of over $37 billion and has completed over 160 platform investments across nine flagship funds. MDP invests across five dedicated industry verticals, including financial services, healthcare, technology and government services.
Equal Employment Opportunity Policy
Great Gray Group, LLC is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status or other non\-merit factor.
Accommodation Statement
Great Gray is committed to ensuring individuals with disabilities and/or those who have special needs participate in the workforce and are afforded equal opportunity to apply and compete for jobs. If you would like to contact us regarding the accessibility of our website, need assistance completing the application process, or need to request an accommodation for any part of our application or interview process, please contact us at: [email protected]
Salary Context
This $90K-$120K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Great Gray Trust Company, 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. This role's midpoint ($105K) sits 46% below the category median. Disclosed range: $90K to $120K.
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.
Great Gray Trust Company AI Hiring
Great Gray Trust Company has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $120K - $120K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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