eBay is hiring a Data Scientist for its Trust & Risk team in Bengaluru, working on a hybrid basis. The role centres on understanding transaction risk, buyer and seller trust, and marketplace losses across a platform serving millions of users in more than 190 markets. You will analyse large datasets, build and monitor KPIs, and turn findings into recommendations for Product, Engineering, Policy, Shipping, and Design teams. It suits analysts with about two years of experience who want to move into risk and fraud analytics at a global ecommerce company, and who are comfortable working with teams in the US time zone.
Most data roles ask you to report on what happened. This one asks you to work out why a buyer lost money, why a seller’s experience broke down, and what the business can change so it does not happen again.
Why This Role Matters
Trust is the product on a marketplace. If buyers doubt they will get what they paid for, or sellers feel unfairly treated by policy decisions, the platform loses both sides. The Trust & Risk team at eBay works on exactly this, covering seller protections, the Money Back Guarantee, buyer and seller feedback, seller performance, new seller onboarding, and transaction risk management.
As a Data Scientist here, you are the person who measures whether these programmes actually work.
Your analysis will help decide where eBay can reduce transaction-related losses while keeping the experience smooth for genuine customers. That balance between risk control and customer experience is the core of the job.
A Typical Day In This Role
Expect a mix of investigation and communication. One morning you might be digging into why a loss metric moved week over week. In the afternoon you may be sitting with a product manager to scope an analysis of a new seller protection policy. Between these, you will be writing queries, checking data quality, and preparing a short summary that a non-technical stakeholder can act on.
You will work with partners across Product, Engineering, Policy, Shipping, and Design. Each group asks different questions, so the ability to translate a vague business concern into a clear analytical problem is used daily.
Core Responsibilities At A Glance
Area | What You Will Actually Do |
|---|---|
Insight generation | Apply quantitative methods to Trust & Risk problems and support operational decisions |
Opportunity analysis | Study marketplace and transaction risk issues to find ways to cut losses and improve trust |
Metrics ownership | Build and monitor KPIs on trust, loss, decision quality, and customer experience |
Driver analysis | Investigate why a metric moved and surface what the business should do about it |
Delivery | Execute analytical projects, measure outcomes, and turn findings into recommendations |
Partnership | Work with Trust, Risk, Product, Engineering, Policy, Shipping, and Design |
What Recruiters May Evaluate
The requirements split into what is expected on day one and what will set you apart.
Skill Area | Expected | Helpful Extra |
|---|---|---|
Education and experience | BA/BS with 2+ years in analytics, quantitative research, or data science, or an advanced degree with relevant experience | Experience at a technology company |
Programming and querying | Python or R , plus SQL | Exposure to NLP or LLM/GenAI work |
Domain knowledge | Ability to read customer, transaction, and business trends | Fraud, transaction risk, or marketplace trust analytics |
Data handling | Comfort with large, complex datasets | Experience with loss-related or risk datasets |
Communication | Clear explanation to technical and non-technical audiences | Presenting complex analysis in simple terms |
Background | Structured problem solving | E-commerce, payments, or shipping |
Shift And Working Environment
This is a Hybrid role based in Bengaluru, and it comes with a schedule detail worth reading carefully.
Meetings run 9:00 AM to 12:00 PM Pacific
That window works out to roughly 9:30 PM to 12:30 AM IST. Regular availability in those hours is required, so it is worth thinking honestly about whether that rhythm suits you before you apply. Candidates who have already worked with US-based teams will find the adjustment easier.
Skills That Will Help You Grow Faster
Technical skill gets you through screening, but the people who grow quickly in risk analytics tend to build three habits.
First, metric thinking. Learn to ask what a number is really measuring, what could make it move without anything real changing, and which segments are hiding the story. Second, trade-off reasoning. Every fraud control has a cost in customer friction, and good analysts can quantify both sides. Third, brevity. A recommendation that fits on one slide is more likely to change a decision than a forty-page deck.
In risk analytics, the strongest analysts are the ones who can explain a loss trend to a policy manager without a single line of code.
Things Worth Learning Before Applying
If you are coming from a general analytics background, a few areas are worth brushing up on before the interview. Revise SQL joins, window functions, and aggregations on large tables. Practise cohort analysis and trend decomposition. Read up on how marketplaces handle buyer protection, chargebacks, and seller performance standards, since this vocabulary will come up in conversation.
The posting also mentions NLP and LLM/GenAI as a plus. You do not need to be an expert, but being able to describe one project where you worked with text data, such as classifying feedback or messages, can set you apart.
What Can Strengthen Your Resume
Recruiters reviewing for this role will look for evidence of independent execution, so describe projects with a clear problem, your method, and a measurable outcome. If you have worked on KPI dashboards, metric investigations, or anomaly analysis, put them near the top. Mention the scale of the data you handled and the stakeholders you presented to. Any exposure to fraud, payments, shipping, or e-commerce should be stated plainly rather than buried.
Long-Term Career Direction
Trust and risk analytics offers a clear path into senior data science, analytics leadership, and specialised fraud or policy science roles. The same skills carry across marketplaces, fintech, and payments companies, which gives you flexibility as your career develops. The listing shows similar openings at lead, manager, and senior manager level in Bengaluru, which suggests the function has room for progression.
Keywords For Resume
Data Science • Trust & Risk • Fraud Analytics • Transaction Risk • SQL • Python • R • KPI Development • Metric Analysis • Loss Reduction • Marketplace Analytics • E-commerce • Stakeholder Communication • Quantitative Analysis • Data Storytelling • NLP • GenAI
Final Thoughts
This is a good fit for analysts with around two years of experience who want their work tied directly to business outcomes like customer trust and loss reduction. The late-evening meeting window is a real commitment, but for candidates who can manage it, the role offers exposure to global stakeholders and a high-visibility problem space.
The above article is written by me, a person interested in technology, automobiles, modern gadgets, movies, music, and clean aesthetics.
