Interested in this Data Scientist role at Volvo Group?
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About This Role
Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.
What you will do
This role partners directly with functional leaders in Finance, Credit, Operations, Legal, and HR to translate business needs into clear technical requirements; collaborates closely with data engineering to design or acquire the right datasets; and leads the model‑to‑product lifecycle with strong attention to Responsible AI and model governance. In addition to delivering high‑impact solutions tied to VFS priorities, the role mentors and guides data scientists, setting technical direction and quality standards while building the foundations for a future formal leadership path.
Key Responsibilities:
- Own the discovery, scoping, and delivery of AI/ML solutions that measurably improve outcomes in VFS business functions.
- Translate open-ended business problems into structured problem statements, success metrics, and prioritized backlogs.
- Partner with data engineers and data operations to design and secure data pipelines and access for AI products.
- Define model architecture and evaluation strategies to productionize models with monitoring, drift detection, and CI/MLOps guardrails aligned with Group AI architecture and governance practices.
- Establish and track business level KPIs and model-level KPIs to ensure sustained value realization.
- Act as a bridge between business and technology.
- Mentor and direct data science team, setting coding standards, review analyses, unblock delivery, and coach on stakeholder engagement.
- Model best practices in research, reproducibility, and documentation.
Your future team
You will report directly to the Manager, Data Quality and Products within VFS D&IT and will be assigned to a stable, global cross-functional team. Digital and IT is the engine of the VFS business, and we continue our strategic journey to further improve performance and to deliver the best customer experience in the industry. In this exciting environment, we are focused on Digital Innovation to meet the demands of our customers in an increasingly digitalized world.
In many of our markets, VFS has won the Great Place to Work award, where Great Work is done. By interacting with each other as an on-site business, we build relationships, which allow us to work together more efficiently and form stronger bonds with our colleagues and customers.
The preferred location can be Wroclaw (Poland), Gothenburg (Sweden), or Greensboro, NC (USA).
Who are you?
Do you dream big? We do too, and we are excited to grow together.
In this role, we believe it is the right mindset that will make a difference at VFS. If you, besides your genuine interest in working with people in Transforming Together, also have a positive, service-oriented, and collaborative mindset, and take a proactive approach to handling all responsibilities, idea generation, and recommendations for productivity and efficiency - then we would welcome you to apply for this role. We believe the successful candidate has the passion and excitement to take on this challenge.
Qualifications likely required to be successful in this role include:
- Experience translating complex business problems into structured analytical and AI solutions.
- Overall information technology experience of a minimum of 5 years
- Bachelor’s Degree in Computer Science, analytics, statistics or equivalent preferred
- Expertise in end-to-end AI product development, from data preparation to production.
- Strong stakeholder management skills, engaging leaders across business functions.
- Proficiency with modern ML/AI frameworks and advanced statistical modeling techniques
- Ability to communicate with team members and guide technical problem solving with clarity.
- Skill in collaborating with data team members to design scalable data pipelines.
- Strength in communicating technical insights through compelling visualization and storytelling.
- Comfort operating in ambiguity and independently driving high-impact initiatives
- Cross-functional collaboration skills for translating functional needs into technical requirements
- Ability to influence technical and strategic decisions across teams and domains.
- Continuous learning mindset to stay current with evolving AI platforms, tools and methods.
At the Volvo Group, we strive for a clear, transparent, and straightforward compensation approach, motivating you to contribute to the company’s growth. For this position, the base pay is set at $111,100.00 - $132,700.00 USD annually, and where applicable, bonus eligible. The range for this role, as well as final salary offered, is determined by several factors including, but not limited to, geographic location, work-related knowledge, certifications, skills, education, and experience. In addition to these factors, we believe in the importance of pay equity and consider internal equity of our current team members as part of any final offer.
In addition to a solid package of compensation and benefits, plus you will enjoy:* Competitive medical, dental and vision insurance.
- Generous paid time off.
- Competitive matching retirement savings plans.
- Working environment where your safety, health and wellbeing come first.
- Focus on professional and personal development through Volvo Group University.
- Programs that make today’s challenging reality of combining work and personal life easier.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, disability, or status as a protected veteran.
We value your data privacy and therefore do not accept applications via mail. Who we are and what we believe in
We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.
Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.
At Volvo Financial Services, we are working together to shape the world we want to live in. As the captive finance arm of the Volvo Group, VFS provides financial services and solutions that meet the needs of our customers’ evolving business. Through our dedication to innovation, we support society in its adoption of sustainable transport and equipment solutions. VFS is headquartered inGothenburg, Sweden, and serves Volvo Group customers and dealers in more than 50 markets.
What’s in it for you?
We offer a solid and competitive package of compensation and benefits, plus you will enjoy a diverse working environment with a culture of care and inclusion. As an investment, we support your personal development and growth to achieve your career aspirations. Volvo Financial Services is an on-site business and we understand that you might need occasional or temporary flexibility, which your leader is equipped to manage and fits well to our culture, as being together enables us to build upon our innovative and collaborative culture, as well as develop you for continued success.
Salary Context
This $111K-$132K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 150 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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Volvo Group, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($121K) sits 36% below the category median. Disclosed range: $111K to $132K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Volvo Group AI Hiring
Volvo Group has 1 open AI role right now. They're hiring across Data Scientist. Based in Greensboro, NC, US. Compensation range: $132K - $132K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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