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About This Role
Ericsson Inc. does not sponsor U.S work authorizations for this job position including U.S. immigration filings for initial and/or change of employer paperwork for H-1's, H-1B1's, E-3's, O-1's, and TN's. Ericsson also does not hire F-1's working on CPT or EAD for this position.
This role will be sitting out of our Santa Clara, CA office and relocation support isnt available at this time.
About this opportunity
The AI Innovation & Incubation (AIII) team in Silicon Valley is a group of applied research professionals using AI and foundation models to address real-world challenges across all layers of the cellular network. The team takes ideas from early exploration through proof-of-concept (PoC), minimum viable product (MVP), and into production-ready solutions used by Ericsson's Business Areas and Market Areas globally. In this role, you will collaborate with the team to explore AI-RAN and RAN-for-AI opportunities, build and train advanced models, and contribute to work that moves from experimentation to real-world impact in telecom networks. This position is based in Silicon Valley, USA, and is suited for someone comfortable with ambiguity who thrives in a fast-moving field.
What you will do
- Design, train, and evaluate deep neural networks — including vision-language models (VLMs) and other foundation models — for telecom-relevant use cases.
- Prototype AI agents and solution pipelines using large language models (LLMs), multimodal LLMs, VLMs, and modern machine learning techniques to improve network performance and operations.
- Translate research into working code by reading leading conference papers (e.g., CVPR, NeurIPS, ICML, ICLR) and reproducing key ideas through experiments.
- Run experiments on cloud platforms (GCP, AWS, Azure) or on-premises GPU clusters as part of scalable training and evaluation workflows.
- Collaborate with applied researchers, engineers, and domain experts to develop PoCs into production-ready solutions.
- Stay current with the evolving AI landscape, evaluate new methods and tools, and identify opportunities to apply them to ongoing work.
The skills you bring
- Master's or PhD in Computer Science, Electrical Engineering, Robotics, or a related field, with 4+ years (MS) or 1+ year (PhD) of relevant industry experience.
- 5+ years of experience working with deep neural networks (industry and/or research).
- Strong Python programming skills and hands-on experience with modern ML frameworks such as PyTorch or JAX.
- Demonstrated ability to read, understand, and implement methods from research papers.
- Experience with at least one cloud platform (GCP, AWS, or Azure).
- Clear communication skills and a collaborative approach to working in a professional team setting.
- Publications in relevant venues (e.g., CVPR, NeurIPS, ICLR), open-source ML contributions, or experience taking projects from PoC to production are a plus.
- Familiarity with telecom, wireless networks, or RAN concepts — or a strong motivation to learn these domains — is preferred.
- Hands-on experience in 3D vision or ROS is a big plus.
- AI is becoming core to how we build and deliver at Ericsson. We're seeking talents who are ready to learn, adapt, and develop the skills needed to thrive in an AI-enabled future.
Why join Ericsson?
At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.
What happens once you apply?
Click Here to find all you need to know about what our typical hiring process looks like.
Ericsson uses a merit-based hiring approach that values people with different experiences, perspectives and skillsets. We truly believe this approach drives innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity employer, learn more.
If you need assistance or to request an accommodation due to a disability, please contact Ericsson at [email protected].
DISCLAIMER: The above statements are intended to describe the general nature and level of work being performed by employees in this position. They are not an exhaustive list of all responsibilities, duties and skills required for this position, and you may be required to perform additional job tasks as assigned.
Primary country and city: United States (US) || Santa Clara (Country/ City)
Job details: Data Scientist (Techn)
Compensation and Benefits at Ericsson
At Ericsson, we know that our people are the key to our success. We offer a competitive package to help with your individual needs and goals.
Your Pay
The salary range for this position is dependent on various factors including, but not limited to, location, and the candidate’s combination of job-related knowledge, qualifications, skills, education, training, and experience.
The salary range for this position is
- LOCATION: $156,000 - $204,225 USD
Short-Term Variable Compensation Plan: Your pay also includes the opportunity for an annual bonus. Actual bonus payouts are based on performance of the business against the unit’s objectives, individual performance, and the individual bonus target. Certain eligibility and pro-ration rules apply.
Your Health
Ericsson offers excellent health benefits including the choice of three medical plan options and a dental plan option that allow an employee to select the level of coverage that suits their needs. Employees will receive company credits in an amount equal to the cost that Ericsson pays toward the cost of their medical and dental premiums for themselves and eligible covered dependents.
Your Financial Security
We invest in both your short and long-term financial wellbeing. The Ericsson US 401(k) Plan offers an automatic 3% company contribution and Ericsson match $1 for every $1 you put into the 401(k) Plan on the first 3% of your eligible pay, plus 50 cents on every $1 on the next 2% of eligible pay. When you contribute at least 5% of eligible pay, you are receiving Ericsson’s full matching contributions of 4%. Matching and company automatic contributions stop when your total eligible pay for the year reaches the IRS limits. Employees will also receive company credits in an amount equal to the cost of basic life insurance and basic accidental death and dismemberment coverage, as well as short-term and long-term disability coverage. Employees also have the option to participate in Ericsson’s Stock Purchase Plan.
Your Time
Your work-life balance is important to us. New employees are provided a minimum of 15 days of accrued vacation, up to 3 personal days per year, 11 annual holidays, 8 hours of volunteer time, and 80 hours of sick time annually. Please note paid time off is pro-rated based on the employee’s start date. Furthermore, Ericsson provides up to 16 weeks of paid maternity leave and 6 weeks of parental or adoption leave at 100% of pay.
Additional Benefits
Ericsson offers many other company-paid benefits such as financial wellness programs, educational assistance, matching gifts, and recognition programs.
Salary Context
This $156K-$204K range is above the median for Data Scientist roles in our dataset (median: $162K across 195 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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Ericsson, 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 1,564 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 5% below the category median. Disclosed range: $156K to $204K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Ericsson AI Hiring
Ericsson has 1 open AI role right now. They're hiring across Data Scientist. Based in Santa Clara, CA, US. Compensation range: $204K - $204K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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