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About This Role
Company Description
WD is building the infrastructure behind the AI\-driven data economy.
As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in.
We combine deep engineering expertise with global\-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide.
This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today.
We’re looking for people who want to build, solve, and operate at that level.
Join us and let’s shape the future of data.
Job Description
As an Engineering Data Scientist, you'll work alongside software engineers, hardware engineers, and data scientists to develop predictive diagnostic technologies for enterprise hard disk drives deployed in hyperscale cloud environments.
You'll help transform more than 10 TB of manufacturing and telemetry data generated each day into intelligent systems that improve storage reliability and customer uptime.
Your work may include:
- Building machine learning models that identify early indicators of drive degradation.
- Developing automated diagnostic algorithms using Python.
- Analyzing large\-scale telemetry from enterprise storage systems.
- Creating data visualization and engineering analysis tools.
- Supporting root cause investigations using statistical analysis.
- Improving existing diagnostic software through automation and code refactoring.
- Collaborating with engineering teams across hardware, firmware, and software disciplines.
- Deploying predictive analytics solutions into production environments.
Why This Role Is Different
Most entry\-level data science positions focus on dashboards, reporting, or business analytics.
This role is different.
You'll use machine learning and engineering principles to solve complex technical problems that improve the reliability of storage systems supporting some of the world's largest cloud environments.
You'll write production software.
You'll automate engineering decisions.
You'll build predictive technologies that help customers avoid downtime before failures occur.
This role is part of WD's early career development program. WD's early career development program is ideal for individuals at the early stages of their professional career. Participants receive foundational training through structured onboarding, mentorship, and a curated development curriculum. The responsibilities of this role are typically aligned with candidates who have approximately 0–2\+ years of relevant professional experience, though candidates of all experience levels are encouraged to apply.
Qualifications
Currently pursuing or recently completed a Ph.D. Computer Science, Data Science, Computer Engineering, Electrical Engineering, Physics, Applied Mathematics, or a related engineering discipline
Required Technical Skills
- Python (required)
- Machine learning
- Data analytics
- Statistical analysis
- SQL or NoSQL databases
- Analytical problem solving
- Software development fundamentals
- Engineering mindset
Preferred:
- Hadoop
- Hive
- Ruby
- Front\-end development
- Web applications
- Data visualization
- Interest in storage systems and HDD technology
Additional Information
WD is committed to providing equal opportunities to all applicants and employees and will not discriminate against any applicant or employee based on their race, color, ancestry, religion (including religious dress and grooming standards), sex (including pregnancy, childbirth or related medical conditions, breastfeeding or related medical conditions), gender (including a person’s gender identity, gender expression, and gender\-related appearance and behavior, whether or not stereotypically associated with the person’s assigned sex at birth), age, national origin, sexual orientation, medical condition, marital status (including domestic partnership status), physical disability, mental disability, medical condition, genetic information, protected medical and family care leave, Civil Air Patrol status, military and veteran status, or other legally protected characteristics. We also prohibit harassment of any individual on any of the characteristics listed above. Our non\-discrimination policy applies to all aspects of employment. We comply with the laws and regulations set forth in the "Know Your Rights: Workplace Discrimination is Illegal” poster. Our pay transparency policy is available here.
WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.
WD is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [email protected] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Based on our experience, we anticipate that the application deadline will be 11/6/26, although we reserve the right to close the application process sooner if we hire an applicant for this position before the application deadline. If we are not able to hire someone from this role before the application deadline, we will update this posting with a new anticipated application deadline.
\#LI\-MT
Compensation \& Benefits Details
- An employee’s pay position within the salary range may be based on several factors including but not limited to (1\) relevant education; qualifications; certifications; and experience; (2\) skills, ability, knowledge of the job; (3\) performance, contribution and results; (4\) geographic location; (5\) shift; (6\) internal and external equity; and (7\) business and organizational needs.
- The salary range is what we believe to be the range of possible compensation for this role at the time of this posting. We may ultimately pay more or less than the posted range and this range is only applicable for jobs to be performed in California, Colorado, New York or remote jobs that can be performed in California, Colorado and New York. This range may be modified in the future.
- If your position is non\-exempt, you are eligible for overtime pay pursuant to company policy and applicable laws. You may also be eligible for shift differential pay, depending on the shift to which you are assigned.
- You will be eligible to be considered for bonuses under either WD’s Short Term Incentive Plan (“STI Plan”) or the Sales Incentive Plan (“SIP”) which provides incentive awards based on Company and individual performance, depending on your role and your performance. You may be eligible to participate in our annual Long\-Term Incentive (LTI) program, which consists of restricted stock units (RSUs) or cash equivalents, pursuant to the terms of the LTI plan. Please note that not all roles are eligible to participate in the LTI program, and not all roles are eligible for equity under the LTI plan. RSU awards are also available to eligible new hires, subject to WD's Standard Terms and Conditions for Restricted Stock Unit Awards.
- We offer a comprehensive package of benefits including paid vacation time; paid sick leave; medical/dental/vision insurance; life, accident and disability insurance; tax\-advantaged flexible spending and health savings accounts; employee assistance program; other voluntary benefit programs such as supplemental life and AD\&D, legal plan, pet insurance, critical illness, accident and hospital indemnity; tuition reimbursement; transit; the Applause Program; employee stock purchase plan; and the WD Savings 401(k) Plan.
- Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
Notice To Candidates: Please be aware that WD and its subsidiaries will never request payment as a condition for applying for a position or receiving an offer of employment. Should you encounter any such requests, please report it immediately to WD Ethics Helpline or email [email protected].
Salary Context
This $140K-$187K range is above the median 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 WD, 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 ($163K) sits 15% below the category median. Disclosed range: $140K to $187K.
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.
WD AI Hiring
WD has 1 open AI role right now. They're hiring across Data Scientist. Based in San Jose, CA, US. Compensation range: $187K - $187K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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