> Markdown version of [/jobs/ext/3039810-senior-data-scientist-field-business-functions](https://www.wearedevelopers.com/jobs/ext/3039810-senior-data-scientist-field-business-functions). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Data Scientist - Field Business Functions

- **Company:** UL
- **Location:** Springfield, IL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $100,000.0 - $130,000.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Agile Methodology, Data Analysis, Microsoft Azure, Data Cleansing, Information Engineering, Data Profiling, R (Programming Language), Python (Programming Language), Machine Learning, Power BI, Cloud Services, SQL Databases, Tableau (Software), Deep Learning, Model Validation, Backend, Data Lakes, Information Technology, Data Analytics
- **Published:** September 23, 2026
- **Apply:** https://www.financialjobbank.com/job.asp?id=3398905625&tx=KJ3939FFF&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelor's degree in data science, Statistics, Mathematics, Computer Science, Analytics, Economics, Engineering, or a related quantitative field.
 * 7 to 10+ years of experience in data science, advanced analytics, decision science, or a closely related role.
 * Proven experience partnering directly with business leaders and senior stakeholders to solve complex business problems and influence decisions.
 * Demonstrated ability to lead discovery, gather and document requirements, define measurable outcomes, and translate ambiguous needs into analytical solutions.
 * Strong business acumen and the ability to connect analytical work to operational, customer, financial, or strategic outcomes.
 * Advanced proficiency in SQL and Python or R for data preparation, exploratory analysis, statistical modeling, and machine learning.
 * Strong foundation in statistical methods, experimentation, predictive modeling, model evaluation, and analytical interpretation.
 * Advanced experience with Power BI, Tableau, or similar tools, including the ability to design clear, decision-oriented visualizations.
 * Excellent written, verbal, presentation, facilitation, listening, influencing, and stakeholder management skills.
 * Experience delivering work in Agile environments and using Azure DevOps or a comparable work management platform.
 * Ability to manage multiple priorities, communicate delivery risks, and maintain quality in a cross-functional enterprise environment., * Master's degree in Data Science, Analytics, Statistics, Business Analytics, Economics, or a related field.
 * Experience developing business cases, defining KPIs, measuring benefits, or evaluating adoption and realized value.
 * Experience with causal inference, forecasting, optimization, NLP, deep learning, or other advanced analytical techniques.
 * Experience working with Salesforce data and reporting.
 * Experience in business services, manufacturing, field operations, or another complex enterprise environment.
 * Familiarity with cloud data platforms, data lakes, and modern enterprise analytics ecosystems.
 * Experience mentoring analytical professionals and raising standards for stakeholder-facing delivery.

## Description

* Build trusted relationships with business leaders, functional teams, and subject matter experts to understand priorities, operating context, constraints, and decision needs.
 * Lead discovery sessions to frame ambiguous business challenges, identify root causes, define hypotheses, and determine where analytics or data science can create meaningful value.
 * Translate business needs into clear problem statements, success measures, business requirements, user stories, and analytical plans.
 * Challenge requests when the proposed solution does not address the underlying business problem and recommend a more effective approach.
 * Align stakeholders on scope, priorities, assumptions, dependencies, risks, and expected outcomes before development begins.

Analytics and Data Science Delivery

 * · Own end-to-end analytics and data science initiatives from discovery and data assessment through modeling, validation, implementation, adoption, and outcome measurement.
 * · Analyze large and complex datasets to identify patterns, drivers, anomalies, opportunities, and actionable insights.
 * · Design, develop, and evaluate statistical, predictive, and machine learning solutions using methods appropriate to the business question.
 * · Define evaluation criteria that include analytical performance, interpretability, operational feasibility, business impact, and risk.
 * · Perform data profiling, cleansing, reconciliation, and validation to ensure analytical outputs are accurate, explainable, and fit for purpose.
 * · Develop decision-support tools, analyses, dashboards, and prototypes using SQL, Python, R, Power BI, Excel, or related tools.

Insight Communication and Influence

 * Synthesize complex analysis into clear business narratives, options, recommendations, and implications for executive and non-technical audiences.
 * Create effective visualizations and presentations that focus on decisions, business impact, tradeoffs, and recommended actions rather than technical detail alone.
 * Facilitate stakeholder reviews, resolve conflicting perspectives, and influence decisions using evidence and sound business judgment.
 * Communicate limitations, assumptions, uncertainty, and risks transparently so stakeholders can make informed decisions.
 * Partner with users to validate outputs, incorporate feedback, drive adoption, and confirm that delivered solutions address the intended business need.

Delivery Leadership and Cross-Functional Collaboration

 * Manage analytical backlogs, priorities, user stories, acceptance criteria, and delivery progress in Azure DevOps within an Agile environment.
 * Coordinate with data engineering, application, product, and platform teams on data availability, solution design, implementation, and production readiness.
 * Provide analytical leadership without assuming primary ownership for backend platform engineering, pipelines, or infrastructure.
 * Mentor Data Scientists and Analysts in business problem framing, stakeholder engagement, analytical rigor, documentation, and insight communication.
 * Establish and promote best practices for reproducibility, model validation, documentation, governance, and responsible use of data and analytics.
 * Identify opportunities to improve processes, reporting, decision workflows, and the organization's broader analytics maturity., * Business Problem Solving - Frames the right problem and connects analytical work to business decisions and outcomes.
 * Stakeholder Leadership - Builds trust, manages expectations, resolves ambiguity, and influences across functions and levels.
 * Analytical Judgment - Selects fit-for-purpose methods and balances rigor, interpretability, speed, and practicality.
 * Executive Communication - Turns complex findings into concise narratives, recommendations, and clear choices.
 * End-to-End Ownership - Maintains accountability from discovery through adoption and outcome measurement.
 * Cross-Functional Collaboration - Works effectively with business, data engineering, technology, and delivery teams.
 * Mentorship and Standards - Develops others and promotes quality, documentation, governance, and continuous improvement.
 * Strategic Thinking - Identifies opportunities and helps shape a longer-term analytics roadmap aligned with business priorities.

What Success Looks Like

 * Business stakeholders view the individual as a trusted analytical partner, not only a technical resource.
 * Projects begin with clearly defined business problems, decision owners, success measures, and expected value.
 * Analytical recommendations are understandable, actionable, and used in business decision-making.
 * Solutions achieve stakeholder adoption and demonstrate measurable operational or strategic impact.
 * Delivery is transparent, risks are raised early, and analytical outputs meet agreed quality and governance standards.
 * Junior team members improve their ability to engage stakeholders, structure problems, and communicate insights., A global leader in applied safety science, UL Solutions (NYSE: ULS) transforms safety, security and sustainability challenges into opportunities for customers in more than 110 countries. UL Solutions delivers testing, inspection and certification services, together with software products and advisory offerings, that support our customers' product innovation and business growth. The UL Mark serves as a recognized symbol of trust in our customers' products and reflects an unwavering commitment to advancing our safety mission. We help our customers innovate, launch new products and services, navigate global markets and complex supply chains, and grow sustainably and responsibly into the future. Our science is your advantage.

UL LLC has been and will continue to be an equal opportunity employer. To assure full implementation of this equal employment policy, we will take steps to assure that


## Related Videos

- [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning)
- [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock)
- [Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents)
- [The Data Mesh as the end of the Datalake as we know it](https://www.wearedevelopers.com/videos/156-the-data-mesh-as-the-end-of-the-datalake-as-we-know-it)

## Related Articles

- [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk)
- [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)
- [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)