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About This Role
Join the Sony Engagement Platform Services (SEPS) Team, where innovation meets collaboration in the dynamic world of digital engagement. At SEPS, we are building the next-generation platform that powers how fans connect with the content—and the creators—they love. Our mission is to unlock meaningful, data-driven engagement across Sony’s entertainment ecosystem, from games and anime to music, film, and live experiences—empowering both internal teams and external partners to build at scale. Our mission is to create a world-class engagement platform that not only enhances existing revenue streams but also drives new opportunities through a robust ecosystem of partners and creators. By decoupling application development from platform services, we empower our teams to deliver exceptional solutions with agility and efficiency. If you are passionate about shaping the future of digital engagement and want to be part of a vibrant community that values creativity and collaboration, we invite you to explore the exciting opportunities within our team. Together, we will shape the future of fan experiences and define the foundation for what’s possible in the realm of engagement. To learn more visit: www.sony.com/en/SonyInfo/technology/stories/entries/sep_introduction/
POSITION SUMMARY
We are looking for a Senior Data Scientist to join a focused team within SEPS Data Science supporting payment, subscription, and risk experiences across PlayStation’s direct-to-consumer business. This is a hands-on role for someone who can use statistics, machine learning, experimentation, and strong data judgment to help teams make better decisions about how players pay, subscribe, and move through global payment flows.
This role will be working closely with the Sony Interactive Entertainment (SIE) Global Payments Team that plays a central role in enabling consumers across the world to purchase hardware, digital content and services.
The initial portfolio is expected to focus on payment method performance, payment flow optimization, subscription payment recovery, and ROI-based evaluation of experiments and business interventions. You will help teams understand customer behavior, payment success, cost and routing tradeoffs, and the business impact of new payment capabilities.
Our team values practical scientific rigor: clear decision framing, trusted reusable metrics, transparent uncertainty, and recommendations that help teams move faster without sacrificing measurement quality. This role is best suited for someone who can lead ambiguous, high-impact data science initiatives, define analytical direction across multiple workstreams, and translate complex evidence into decisions that shape customer experience and business strategy.
JOB RESPONSIBILITIES
- Lead high-impact data science initiatives across payments, subscriptions, commerce, fraud, risk, and player experience, translating ambiguous business needs into analytical strategies.
- Define experimental, causal-inference, statistical-modeling, and machine-learning approaches for complex decisions with broad business impact.
- Develop scalable models, measurement frameworks, and reusable analytical capabilities while setting standards for rigor, validation, and reproducibility.
- Influence product and business roadmaps by advising senior stakeholders on opportunities, risks, investment tradeoffs, and expected outcomes.
- Provide technical leadership through mentorship, peer review, methodology development, and cross-functional guidance without direct people-management responsibility.
Honesty, trustworthiness and ethical conduct are material requirements for the responsibilities outlined above *
QUALIFICATIONS FOR POSITION
Your qualifications and experience should include:
- 5+ years of professional experience in data science, applied statistics, machine learning, or a related quantitative discipline.
- Bachelor’s or advanced degree in a quantitative field, or equivalent practical experience.
- Advanced SQL and Python skills with deep expertise in experimentation, causal inference, statistical modeling, machine learning, and model evaluation.
- Demonstrated success leading complex data science initiatives from problem framing through operationalization and impact measurement.
Proven ability to influence senior technical and business stakeholders and elevate data science practices across teams.
*
Preferred Qualifications
- Advanced degree in quantitative field.
- Experience in payments, subscriptions, digital commerce, fintech, gaming, media, marketplaces, or scaled consumer technology.
- Deep expertise in experimentation, payment optimization, subscription analytics, forecasting, segmentation, churn, retention, or customer lifetime value.
- Experience operationalizing models and working with large-scale platforms such as Snowflake, Databricks, Spark, or BigQuery.
- Experience creating metric layers, source-of-truth datasets, analytical frameworks, or reusable data science tooling.
- Lead high-impact data science initiatives across payments, subscriptions, commerce, fraud, risk, and player experience, translating ambiguous business needs into analytical strategies.
- Define experimental, causal-inference, statistical-modeling, and machine-learning approaches for complex decisions with broad business impact.
- Develop scalable models, measurement frameworks, and reusable analytical capabilities while setting standards for rigor, validation, and reproducibility.
- Influence product and business roadmaps by advising senior stakeholders on opportunities, risks, investment tradeoffs, and expected outcomes.
Provide technical leadership through mentorship, peer review, methodology development, and cross-functional guidance without direct people-management responsibility.
*
We are committed to creating an inclusive employee experience for you to thrive as part of Sony’s purpose to “fill the world with emotion through the power of creativity and technology.”
Benefits:
SCA offers benefits-eligible employees (generally regular employees scheduled to work 20 or more hours a week) a comprehensive benefits program that offers coverage and support for employees and their family’s physical, emotional, and financial well-being.
What we offer you:
- Comprehensive medical, prescription drug, dental, and vision coverage with coverage for spouses/domestic partners and child dependents, including access to a Health Savings Account (HSA) and Flexible Spending Account (FSA)
- Employee assistance plan and comprehensive behavioral health benefits
- Fertility benefits, including, surrogacy, and adoption assistance programs
- Basic and supplemental life insurance for employees as well as supplemental life insurance coverage for their spouses/domestic partners and children
- Voluntary benefits such as group legal, identity theft protection, accident, and hospital indemnity insurance
- Short-term & long-term disability plans
- Up to 12 weeks of paid parental and caregiver leave
- 401(k) Plan with pre-tax, Roth, and after-tax options and company match with immediate vesting
Education assistance and student loan programs
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Other Programs:
- Time off to include vacation, paid holidays, sick leave, Summer Fridays (early release), and a winter break between Christmas and New Year’s Day (based on business needs)
- Referral bonuses (subject to eligibility)
- Matching gift program
- A wide variety of employee business resource groups (EBRGs)
- Special discounts on Sony products, offered exclusively to Sony employees
Employee stock purchase plan (Sony covers commissions and fees for your Sony stock purchases made through after-tax payroll deductions)
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The anticipated annual base salary for this position is $180,000 to $205,000. This range does not include any other compensation components or other benefits that an individual may be eligible for. The actual base salary offered depends on a variety of factors, which may include as applicable, the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job.
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SCA will consider qualified applicants with arrest or conviction records in accordance with applicable law.
All qualified applicants will receive consideration for employment without regard to any basis protected by applicable federal, state, or local law, ordinance, or regulation.
Disability Accommodation for Applicants to Sony Corporation of America
Sony Corporation of America provides reasonable accommodation for qualified individuals with disabilities and disabled veterans in job application procedures. For reasonable accommodation requests, please contact us by email at [email protected] or by mail to: Sony Corporation of America, People Experience Department, 25 Madison Avenue, New York, NY 10010. Please indicate the position you are applying for.
We are aware that unauthorized individuals or organizations may attempt to solicit personal information or payments from job applicants by impersonating our company through fraudulent job postings. We take these matters seriously but cannot control third-party websites. To protect your personal information, please verify that any job posting you respond to also appears on our official Careers page: www.sonyjobs.com . Please also be advised that we never request personal identifying information (such as Social Security numbers, bank details, or copies of identification documents) during the initial stages of our application process. If you have any doubts about the authenticity of a job posting or communication, please contact [email protected] before submitting any information.
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Salary Context
This $180K-$205K range is above the median 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 Sony, 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. Disclosed range: $180K to $205K.
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.
Sony AI Hiring
Sony has 1 open AI role right now. They're hiring across Data Scientist. Based in San Diego, CA, US. Compensation range: $205K - $205K.
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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